{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8adc4034",
   "metadata": {},
   "source": [
    "Empowerment Paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b23382f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output directory: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\n",
      "Figures directory: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\figures\n",
      "\n",
      "==================================================\n",
      "Processing country: Ethiopia (ETH)\n",
      "==================================================\n",
      "Successfully read file. Shape: (11074, 1088)\n",
      "Found component variables: ['aweai_prod2', 'aweai_asset2', 'aweai_cred2', 'aweai_inc2', 'aweai_time2']\n",
      "\n",
      "Empowerment data by wave:\n",
      "     count     sum      mean\n",
      "t                           \n",
      "1.0   2394   206.0  0.086048\n",
      "2.0   3009  1331.0  0.442340\n",
      "3.0   2911  1272.0  0.436963\n",
      "4.0   1918  1053.0  0.549009\n",
      "Valid waves for analysis: [np.float64(1.0), np.float64(2.0), np.float64(3.0), np.float64(4.0), np.float64(nan)]\n",
      "\n",
      "Special handling for Ethiopia:\n",
      "  - For classification: Using waves [2.0, 3.0]\n",
      "  - For other analyses: Using waves [2.0, 3.0, 4.0]\n",
      "Classification dataset shape: (5920, 1088)\n",
      "Analysis dataset shape: (8679, 1088)\n",
      "Households with data in all 2 classification waves: 2770\n",
      "Classification panel shape: (5540, 1088)\n",
      "\n",
      "Performing basic empowerment classification...\n",
      "Basic classification results:\n",
      "  Always empowered: 693 households\n",
      "  Never empowered: 1015 households\n",
      "  Mixed pattern: 1062 households\n",
      "\n",
      "Performing detailed classification of mixed households...\n",
      "Sample households:\n",
      "  ID 1000.0: waves=[2.0, 3.0], emp=[1.0, 1.0]\n",
      "  ID 1001.0: waves=[2.0, 3.0], emp=[1.0, 1.0]\n",
      "  ID 1003.0: waves=[2.0, 3.0], emp=[1.0, 0.0]\n",
      "  ID 1004.0: waves=[2.0, 3.0], emp=[1.0, 0.0]\n",
      "  ID 1005.0: waves=[2.0, 3.0], emp=[1.0, 1.0]\n",
      "Mixed households classification (standard):\n",
      "  Became empowered: 515 (48.5%)\n",
      "  Became disempowered: 547 (51.5%)\n",
      "  Fluctuating: 0 (0.0%)\n",
      "Mixed households classification (relaxed):\n",
      "  Became empowered: 515 (48.5%)\n",
      "  Became disempowered: 547 (51.5%)\n",
      "  Fluctuating: 0 (0.0%)\n",
      "\n",
      "Calculating empowerment means by wave...\n",
      "  Wave 2.0: mean=0.4423, std=0.4967, n=3009\n",
      "  Wave 3.0: mean=0.4370, std=0.4961, n=2911\n",
      "  Wave 4.0: mean=0.5490, std=0.4977, n=2759\n",
      "\n",
      "Analyzing empowerment components...\n",
      "Standardized components: {'prod': 'aweai_prod2', 'inc': 'aweai_inc2', 'asset': 'aweai_asset2', 'cred': 'aweai_cred2', 'time': 'aweai_time2', 'loan': 'aweai_cred2'}\n",
      "\n",
      "Contributions to disempowerment in Wave 2.0:\n",
      "  prod: 23.76%\n",
      "  inc: 22.52%\n",
      "  time: 21.52%\n",
      "  asset: 15.17%\n",
      "  cred: 8.51%\n",
      "  loan: 8.51%\n",
      "\n",
      "Contributions to disempowerment in Wave 3.0:\n",
      "  prod: 23.90%\n",
      "  inc: 22.66%\n",
      "  time: 21.45%\n",
      "  asset: 15.06%\n",
      "  cred: 8.46%\n",
      "  loan: 8.46%\n",
      "\n",
      "Contributions to disempowerment in Wave 4.0:\n",
      "  prod: 24.57%\n",
      "  inc: 21.78%\n",
      "  time: 21.38%\n",
      "  asset: 15.03%\n",
      "  cred: 8.62%\n",
      "  loan: 8.62%\n",
      "\n",
      "Analyzing changes in empowerment over time...\n",
      "Empowerment rate change from Wave 2.0 to Wave 4.0:\n",
      "  0.4423 -> 0.5490 (Change: 0.1067, 24.1%)\n",
      "Component changes from Wave 2.0 to Wave 4.0:\n",
      "  prod: 0.1620 -> 0.1659 (Change: 0.0039, 2.4%)\n",
      "  inc: 0.1885 -> 0.2138 (Change: 0.0252, 13.4%)\n",
      "  asset: 0.1452 -> 0.1542 (Change: 0.0091, 6.2%)\n",
      "  cred: 0.0282 -> 0.0139 (Change: -0.0143, -50.6%)\n",
      "  time: 0.2098 -> 0.2208 (Change: 0.0110, 5.3%)\n",
      "  loan: 0.0282 -> 0.0139 (Change: -0.0143, -50.6%)\n",
      "\n",
      "==================================================\n",
      "Processing country: Malawi (MLW)\n",
      "==================================================\n",
      "Successfully read file. Shape: (7187, 1041)\n",
      "Found component variables: ['aweai_prod', 'aweai_inc', 'aweai_time', 'aweai_asset', 'aweai_asset_loan', 'aweai_norm']\n",
      "\n",
      "Empowerment data by wave:\n",
      "     count     sum      mean\n",
      "t                           \n",
      "1.0   1284   504.0  0.392523\n",
      "2.0   1556   897.0  0.576478\n",
      "3.0   1917  1183.0  0.617110\n",
      "4.0   2341  1630.0  0.696284\n",
      "Valid waves for analysis: [np.float32(1.0), np.float32(2.0), np.float32(3.0), np.float32(4.0)]\n",
      "Classification dataset shape: (7187, 1041)\n",
      "Analysis dataset shape: (7187, 1041)\n",
      "Households with data in all 4 classification waves: 1018\n",
      "Classification panel shape: (4072, 1041)\n",
      "\n",
      "Performing basic empowerment classification...\n",
      "Basic classification results:\n",
      "  Always empowered: 157 households\n",
      "  Never empowered: 58 households\n",
      "  Mixed pattern: 803 households\n",
      "\n",
      "Performing detailed classification of mixed households...\n",
      "Sample households:\n",
      "  ID 1.0: waves=[1.0, 2.0, 3.0, 4.0], emp=[1.0, 1.0, 1.0, 1.0]\n",
      "  ID 2.0: waves=[1.0, 2.0, 3.0, 4.0], emp=[0.0, 0.0, 0.0, 1.0]\n",
      "  ID 3.0: waves=[1.0, 2.0, 3.0, 4.0], emp=[1.0, 1.0, 0.0, 1.0]\n",
      "  ID 5.0: waves=[1.0, 2.0, 3.0, 4.0], emp=[0.0, 0.0, 0.0, 1.0]\n",
      "  ID 9.0: waves=[1.0, 2.0, 3.0, 4.0], emp=[1.0, 1.0, 0.0, 1.0]\n",
      "Mixed households classification (standard):\n",
      "  Became empowered: 244 (30.3%)\n",
      "  Became disempowered: 37 (4.6%)\n",
      "  Fluctuating: 524 (65.1%)\n",
      "Mixed households classification (relaxed):\n",
      "  Became empowered: 319 (39.6%)\n",
      "  Became disempowered: 71 (8.8%)\n",
      "  Fluctuating: 415 (51.6%)\n",
      "\n",
      "Calculating empowerment means by wave...\n",
      "  Wave 1.0: mean=0.3925, std=0.4885, n=1304\n",
      "  Wave 2.0: mean=0.5765, std=0.4943, n=1599\n",
      "  Wave 3.0: mean=0.6171, std=0.4862, n=1935\n",
      "  Wave 4.0: mean=0.6963, std=0.4600, n=2349\n",
      "\n",
      "Analyzing empowerment components...\n",
      "Standardized components: {'prod': 'aweai_prod', 'inc': 'aweai_inc', 'asset': 'aweai_asset', 'loan': 'aweai_asset_loan', 'time': 'aweai_time', 'cred': 'aweai_asset_loan'}\n",
      "\n",
      "Contributions to disempowerment in Wave 1.0:\n",
      "  inc: 24.28%\n",
      "  prod: 23.04%\n",
      "  time: 19.88%\n",
      "  asset: 15.55%\n",
      "  loan: 8.62%\n",
      "  cred: 8.62%\n",
      "\n",
      "Contributions to disempowerment in Wave 2.0:\n",
      "  inc: 25.10%\n",
      "  prod: 21.56%\n",
      "  time: 20.37%\n",
      "  asset: 15.50%\n",
      "  loan: 8.74%\n",
      "  cred: 8.74%\n",
      "\n",
      "Contributions to disempowerment in Wave 3.0:\n",
      "  inc: 25.17%\n",
      "  prod: 21.17%\n",
      "  time: 20.25%\n",
      "  asset: 16.04%\n",
      "  loan: 8.69%\n",
      "  cred: 8.69%\n",
      "\n",
      "Contributions to disempowerment in Wave 4.0:\n",
      "  inc: 24.41%\n",
      "  prod: 21.69%\n",
      "  time: 20.51%\n",
      "  asset: 16.07%\n",
      "  loan: 8.66%\n",
      "  cred: 8.66%\n",
      "\n",
      "Analyzing changes in empowerment over time...\n",
      "Empowerment rate change from Wave 1.0 to Wave 4.0:\n",
      "  0.3925 -> 0.6963 (Change: 0.3038, 77.4%)\n",
      "Component changes from Wave 1.0 to Wave 4.0:\n",
      "  prod: 0.1643 -> 0.2248 (Change: 0.0605, 36.8%)\n",
      "  inc: 0.1347 -> 0.1906 (Change: 0.0559, 41.5%)\n",
      "  asset: 0.1244 -> 0.1131 (Change: -0.0113, -9.1%)\n",
      "  loan: 0.0045 -> 0.0149 (Change: 0.0104, 228.3%)\n",
      "  time: 0.2389 -> 0.2355 (Change: -0.0034, -1.4%)\n",
      "  cred: 0.0045 -> 0.0149 (Change: 0.0104, 228.3%)\n",
      "\n",
      "==================================================\n",
      "Processing country: Tanzania (TZN)\n",
      "==================================================\n",
      "Successfully read file. Shape: (10830, 1350)\n",
      "Found component variables: ['aweai_prod2', 'aweai_asset2', 'aweai_cred2', 'aweai_inc2', 'aweai_time2']\n",
      "\n",
      "Empowerment data by wave:\n",
      "     count     sum      mean\n",
      "t                           \n",
      "1.0      0     0.0       NaN\n",
      "2.0   2205  1521.0  0.689796\n",
      "3.0   2226   859.0  0.385894\n",
      "4.0   2028  1172.0  0.577909\n",
      "5.0   2185  1053.0  0.481922\n",
      "Warning: Waves with no empowerment data: [1.0]\n",
      "Valid waves for analysis: [np.float32(2.0), np.float32(3.0), np.float32(4.0), np.float32(5.0)]\n",
      "Classification dataset shape: (8644, 1350)\n",
      "Analysis dataset shape: (8644, 1350)\n",
      "Households with data in all 4 classification waves: 547\n",
      "Classification panel shape: (2188, 1350)\n",
      "\n",
      "Performing basic empowerment classification...\n",
      "Basic classification results:\n",
      "  Always empowered: 62 households\n",
      "  Never empowered: 33 households\n",
      "  Mixed pattern: 452 households\n",
      "\n",
      "Performing detailed classification of mixed households...\n",
      "Sample households:\n",
      "  ID 1.0: waves=[2.0, 3.0, 4.0, 5.0], emp=[1.0, 0.0, 1.0, 0.0]\n",
      "  ID 5.0: waves=[2.0, 3.0, 4.0, 5.0], emp=[1.0, 0.0, 0.0, 0.0]\n",
      "  ID 6.0: waves=[2.0, 3.0, 4.0, 5.0], emp=[1.0, 0.0, 1.0, 1.0]\n",
      "  ID 7.0: waves=[2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, 1.0]\n",
      "  ID 8.0: waves=[2.0, 3.0, 4.0, 5.0], emp=[0.0, 1.0, 1.0, 0.0]\n",
      "Mixed households classification (standard):\n",
      "  Became empowered: 56 (12.4%)\n",
      "  Became disempowered: 93 (20.6%)\n",
      "  Fluctuating: 303 (67.0%)\n",
      "Mixed households classification (relaxed):\n",
      "  Became empowered: 79 (17.5%)\n",
      "  Became disempowered: 137 (30.3%)\n",
      "  Fluctuating: 236 (52.2%)\n",
      "\n",
      "Calculating empowerment means by wave...\n",
      "  Wave 2.0: mean=0.6898, std=0.4627, n=2205\n",
      "  Wave 3.0: mean=0.3859, std=0.4869, n=2226\n",
      "  Wave 4.0: mean=0.5779, std=0.4940, n=2028\n",
      "  Wave 5.0: mean=0.4819, std=0.4998, n=2185\n",
      "\n",
      "Analyzing empowerment components...\n",
      "Standardized components: {'prod': 'aweai_prod2', 'inc': 'aweai_inc2', 'asset': 'aweai_asset2', 'cred': 'aweai_cred2', 'time': 'aweai_time2', 'loan': 'aweai_cred2'}\n",
      "\n",
      "Contributions to disempowerment in Wave 2.0:\n",
      "  inc: 24.58%\n",
      "  prod: 23.53%\n",
      "  time: 20.25%\n",
      "  asset: 14.96%\n",
      "  cred: 8.34%\n",
      "  loan: 8.34%\n",
      "\n",
      "Contributions to disempowerment in Wave 3.0:\n",
      "  inc: 23.96%\n",
      "  time: 22.35%\n",
      "  prod: 21.13%\n",
      "  asset: 15.47%\n",
      "  cred: 8.54%\n",
      "  loan: 8.54%\n",
      "\n",
      "Contributions to disempowerment in Wave 4.0:\n",
      "  inc: 24.68%\n",
      "  prod: 21.70%\n",
      "  time: 21.12%\n",
      "  asset: 15.47%\n",
      "  cred: 8.51%\n",
      "  loan: 8.51%\n",
      "\n",
      "Contributions to disempowerment in Wave 5.0:\n",
      "  inc: 24.60%\n",
      "  prod: 22.17%\n",
      "  time: 20.98%\n",
      "  asset: 15.37%\n",
      "  cred: 8.44%\n",
      "  loan: 8.44%\n",
      "\n",
      "Analyzing changes in empowerment over time...\n",
      "Empowerment rate change from Wave 2.0 to Wave 5.0:\n",
      "  0.6898 -> 0.4819 (Change: -0.2079, -30.1%)\n",
      "Component changes from Wave 2.0 to Wave 5.0:\n",
      "  prod: 0.1765 -> 0.1880 (Change: 0.0115, 6.5%)\n",
      "  inc: 0.1226 -> 0.1301 (Change: 0.0075, 6.1%)\n",
      "  asset: 0.1997 -> 0.1247 (Change: -0.0749, -37.5%)\n",
      "  cred: 0.0000 -> 0.0174 (Change: 0.0174, nan%)\n",
      "  time: 0.2215 -> 0.2086 (Change: -0.0130, -5.9%)\n",
      "  loan: 0.0000 -> 0.0174 (Change: 0.0174, nan%)\n",
      "\n",
      "==================================================\n",
      "Processing country: Uganda (UGD)\n",
      "==================================================\n",
      "Successfully read file. Shape: (11384, 1148)\n",
      "Found component variables: ['aweai_prod', 'aweai_inc', 'aweai_asset', 'aweai_time']\n",
      "\n",
      "Empowerment data by wave:\n",
      "     count     sum      mean\n",
      "t                           \n",
      "1.0   1978  1631.0  0.824570\n",
      "2.0   1384  1071.0  0.773844\n",
      "3.0   2286  1818.0  0.795276\n",
      "4.0   2000  1709.0  0.854500\n",
      "5.0   1165  1048.0  0.899571\n",
      "Valid waves for analysis: [np.float32(1.0), np.float32(2.0), np.float32(3.0), np.float32(4.0), np.float32(5.0)]\n",
      "Classification dataset shape: (11384, 1148)\n",
      "Analysis dataset shape: (11384, 1148)\n",
      "Households with data in all 5 classification waves: 888\n",
      "Classification panel shape: (4440, 1148)\n",
      "\n",
      "Performing basic empowerment classification...\n",
      "Basic classification results:\n",
      "  Always empowered: 516 households\n",
      "  Never empowered: 18 households\n",
      "  Mixed pattern: 354 households\n",
      "\n",
      "Performing detailed classification of mixed households...\n",
      "Sample households:\n",
      "  ID 1033000301.0: waves=[1.0, 2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, 0.0, 0.0]\n",
      "  ID 1033000307.0: waves=[1.0, 2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, 1.0, 0.0]\n",
      "  ID 1033000308.0: waves=[1.0, 2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "  ID 1033000504.0: waves=[1.0, 2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, nan, nan]\n",
      "  ID 1033000505.0: waves=[1.0, 2.0, 3.0, 4.0, 5.0], emp=[1.0, 1.0, 1.0, 0.0, 1.0]\n",
      "Mixed households classification (standard):\n",
      "  Became empowered: 60 (15.5%)\n",
      "  Became disempowered: 66 (17.0%)\n",
      "  Fluctuating: 262 (67.5%)\n",
      "Mixed households classification (relaxed):\n",
      "  Became empowered: 63 (16.2%)\n",
      "  Became disempowered: 77 (19.8%)\n",
      "  Fluctuating: 248 (63.9%)\n",
      "\n",
      "Calculating empowerment means by wave...\n",
      "  Wave 1.0: mean=0.8246, std=0.3804, n=2091\n",
      "  Wave 2.0: mean=0.7738, std=0.4185, n=2181\n",
      "  Wave 3.0: mean=0.7953, std=0.4036, n=2439\n",
      "  Wave 4.0: mean=0.8545, std=0.3527, n=2441\n",
      "  Wave 5.0: mean=0.8996, std=0.3007, n=2232\n",
      "\n",
      "Analyzing empowerment components...\n",
      "Standardized components: {'prod': 'aweai_prod', 'inc': 'aweai_inc', 'asset': 'aweai_asset', 'time': 'aweai_time'}\n",
      "\n",
      "Contributions to disempowerment in Wave 1.0:\n",
      "  inc: 29.71%\n",
      "  time: 26.49%\n",
      "  prod: 25.80%\n",
      "  asset: 18.00%\n",
      "\n",
      "Contributions to disempowerment in Wave 2.0:\n",
      "  inc: 29.30%\n",
      "  time: 27.17%\n",
      "  prod: 25.97%\n",
      "  asset: 17.57%\n",
      "\n",
      "Contributions to disempowerment in Wave 3.0:\n",
      "  inc: 29.43%\n",
      "  prod: 27.07%\n",
      "  time: 25.34%\n",
      "  asset: 18.16%\n",
      "\n",
      "Contributions to disempowerment in Wave 4.0:\n",
      "  inc: 29.73%\n",
      "  prod: 28.19%\n",
      "  time: 22.82%\n",
      "  asset: 19.26%\n",
      "\n",
      "Contributions to disempowerment in Wave 5.0:\n",
      "  inc: 29.63%\n",
      "  prod: 26.68%\n",
      "  time: 24.70%\n",
      "  asset: 18.99%\n",
      "\n",
      "Analyzing changes in empowerment over time...\n",
      "Empowerment rate change from Wave 1.0 to Wave 5.0:\n",
      "  0.8246 -> 0.8996 (Change: 0.0750, 9.1%)\n",
      "Component changes from Wave 1.0 to Wave 5.0:\n",
      "  prod: 0.2315 -> 0.2358 (Change: 0.0043, 1.9%)\n",
      "  inc: 0.1749 -> 0.1850 (Change: 0.0101, 5.7%)\n",
      "  asset: 0.2168 -> 0.2159 (Change: -0.0009, -0.4%)\n",
      "  time: 0.1584 -> 0.2268 (Change: 0.0685, 43.2%)\n",
      "\n",
      "==================================================\n",
      "Processing country: Nigeria (NGR)\n",
      "==================================================\n",
      "Successfully read file. Shape: (12844, 1249)\n",
      "Found component variables: ['aweai_prod2', 'aweai_asset2', 'aweai_cred2', 'aweai_inc2', 'aweai_time2']\n",
      "\n",
      "Empowerment data by wave:\n",
      "     count     sum      mean\n",
      "t                           \n",
      "1.0      0     0.0       NaN\n",
      "2.0   2997  1569.0  0.523524\n",
      "3.0   2881  1733.0  0.601527\n",
      "4.0   3872  2552.0  0.659091\n",
      "Warning: Waves with no empowerment data: [1.0]\n",
      "Valid waves for analysis: [np.float64(2.0), np.float64(3.0), np.float64(4.0)]\n",
      "Classification dataset shape: (9757, 1249)\n",
      "Analysis dataset shape: (9757, 1249)\n",
      "Households with data in all 3 classification waves: 795\n",
      "Classification panel shape: (2385, 1249)\n",
      "\n",
      "Performing basic empowerment classification...\n",
      "Basic classification results:\n",
      "  Always empowered: 240 households\n",
      "  Never empowered: 100 households\n",
      "  Mixed pattern: 455 households\n",
      "\n",
      "Performing detailed classification of mixed households...\n",
      "Sample households:\n",
      "  ID 10057.0: waves=[2.0, 3.0, 4.0], emp=[1.0, 1.0, 0.0]\n",
      "  ID 10061.0: waves=[2.0, 3.0, 4.0], emp=[1.0, 1.0, 1.0]\n",
      "  ID 10062.0: waves=[2.0, 3.0, 4.0], emp=[1.0, 1.0, 1.0]\n",
      "  ID 10063.0: waves=[2.0, 3.0, 4.0], emp=[0.0, 0.0, 1.0]\n",
      "  ID 10064.0: waves=[2.0, 3.0, 4.0], emp=[0.0, 1.0, 0.0]\n",
      "Mixed households classification (standard):\n",
      "  Became empowered: 113 (24.8%)\n",
      "  Became disempowered: 29 (6.4%)\n",
      "  Fluctuating: 313 (68.8%)\n",
      "Mixed households classification (relaxed):\n",
      "  Became empowered: 234 (51.4%)\n",
      "  Became disempowered: 121 (26.6%)\n",
      "  Fluctuating: 100 (22.0%)\n",
      "\n",
      "Calculating empowerment means by wave...\n",
      "  Wave 2.0: mean=0.5235, std=0.4995, n=2997\n",
      "  Wave 3.0: mean=0.6015, std=0.4897, n=2888\n",
      "  Wave 4.0: mean=0.6591, std=0.4741, n=3872\n",
      "\n",
      "Analyzing empowerment components...\n",
      "Standardized components: {'prod': 'aweai_prod2', 'inc': 'aweai_inc2', 'asset': 'aweai_asset2', 'cred': 'aweai_cred2', 'time': 'aweai_time2', 'loan': 'aweai_cred2'}\n",
      "\n",
      "Contributions to disempowerment in Wave 2.0:\n",
      "  inc: 24.50%\n",
      "  prod: 23.38%\n",
      "  time: 20.50%\n",
      "  asset: 15.00%\n",
      "  cred: 8.31%\n",
      "  loan: 8.31%\n",
      "\n",
      "Contributions to disempowerment in Wave 3.0:\n",
      "  inc: 24.72%\n",
      "  prod: 24.01%\n",
      "  time: 20.30%\n",
      "  asset: 14.98%\n",
      "  cred: 7.99%\n",
      "  loan: 7.99%\n",
      "\n",
      "Contributions to disempowerment in Wave 4.0:\n",
      "  inc: 25.18%\n",
      "  prod: 22.56%\n",
      "  time: 20.66%\n",
      "  asset: 14.96%\n",
      "  cred: 8.32%\n",
      "  loan: 8.32%\n",
      "\n",
      "Analyzing changes in empowerment over time...\n",
      "Empowerment rate change from Wave 2.0 to Wave 4.0:\n",
      "  0.5235 -> 0.6591 (Change: 0.1356, 25.9%)\n",
      "Component changes from Wave 2.0 to Wave 4.0:\n",
      "  prod: 0.1681 -> 0.2071 (Change: 0.0390, 23.2%)\n",
      "  inc: 0.1354 -> 0.1295 (Change: -0.0059, -4.3%)\n",
      "  asset: 0.1268 -> 0.1490 (Change: 0.0222, 17.5%)\n",
      "  cred: 0.0356 -> 0.0521 (Change: 0.0165, 46.5%)\n",
      "  time: 0.2134 -> 0.2249 (Change: 0.0116, 5.4%)\n",
      "  loan: 0.0356 -> 0.0521 (Change: 0.0165, 46.5%)\n",
      "\n",
      "==================================================\n",
      "Creating cash crop decision-making analysis...\n",
      "\n",
      "==================================================\n",
      "Creating cash crop decision-making analysis (sellers only)...\n",
      "\n",
      "Analyzing cash crop decision-making for Ethiopia\n",
      "  First wave with seller data: 2.0\n",
      "  Last wave with seller data: 3.0\n",
      "    Wave 2.0: 2120 sellers out of 3009 households (70.5%)\n",
      "      Cashhouse=0: 830 households (39.2% of sellers)\n",
      "      Cashhouse=1: 1290 households (60.8% of sellers)\n",
      "    Wave 3.0: 1984 sellers out of 2911 households (68.2%)\n",
      "      Cashhouse=0: 1117 households (56.3% of sellers)\n",
      "      Cashhouse=1: 867 households (43.7% of sellers)\n",
      "\n",
      "Analyzing cash crop decision-making for Malawi\n",
      "  First wave with seller data: 1.0\n",
      "  Last wave with seller data: 4.0\n",
      "    Wave 1.0: 668 sellers out of 1304 households (51.2%)\n",
      "      Cashhouse=0: 135 households (20.2% of sellers)\n",
      "      Cashhouse=1: 533 households (79.8% of sellers)\n",
      "    Wave 4.0: 1230 sellers out of 2349 households (52.4%)\n",
      "      Cashhouse=0: 218 households (17.7% of sellers)\n",
      "      Cashhouse=1: 1012 households (82.3% of sellers)\n",
      "\n",
      "Analyzing cash crop decision-making for Tanzania\n",
      "  First wave with seller data: 2.0\n",
      "  Last wave with seller data: 5.0\n",
      "    Wave 2.0: 1252 sellers out of 2205 households (56.8%)\n",
      "      Cashhouse=0: 532 households (42.5% of sellers)\n",
      "      Cashhouse=1: 720 households (57.5% of sellers)\n",
      "    Wave 5.0: 1211 sellers out of 2185 households (55.4%)\n",
      "      Cashhouse=0: 571 households (47.2% of sellers)\n",
      "      Cashhouse=1: 640 households (52.8% of sellers)\n",
      "\n",
      "Analyzing cash crop decision-making for Uganda\n",
      "  First wave with seller data: 1.0\n",
      "  Last wave with seller data: 5.0\n",
      "    Wave 1.0: 1439 sellers out of 2091 households (68.8%)\n",
      "      Cashhouse=0: 161 households (11.2% of sellers)\n",
      "      Cashhouse=1: 1198 households (83.3% of sellers)\n",
      "    Wave 5.0: 1392 sellers out of 2232 households (62.4%)\n",
      "      Cashhouse=0: 140 households (10.1% of sellers)\n",
      "      Cashhouse=1: 607 households (43.6% of sellers)\n",
      "\n",
      "Analyzing cash crop decision-making for Nigeria\n",
      "  First wave with seller data: 2.0\n",
      "  Last wave with seller data: 4.0\n",
      "    Wave 2.0: 1495 sellers out of 2997 households (49.9%)\n",
      "      Cashhouse=0: 187 households (12.5% of sellers)\n",
      "      Cashhouse=1: 1308 households (87.5% of sellers)\n",
      "    Wave 4.0: 1978 sellers out of 3872 households (51.1%)\n",
      "      Cashhouse=0: 349 households (17.6% of sellers)\n",
      "      Cashhouse=1: 1629 households (82.4% of sellers)\n",
      "\n",
      "==================================================\n",
      "Creating output tables...\n",
      "\n",
      "Creating academic format t-test tables...\n",
      "\n",
      "Creating academic format t-test tables...\n",
      "\n",
      "Processing t-tests for Ethiopia_Malawi\n",
      "Created academic table for Ethiopia_Malawi\n",
      "\n",
      "Processing t-tests for Tanzania_Uganda\n",
      "Created academic table for Tanzania_Uganda\n",
      "\n",
      "Processing t-tests for Nigeria\n",
      "Created academic table for Nigeria\n",
      "\n",
      "==================================================\n",
      "Extracting Shapley decomposition from component contributions...\n",
      "\n",
      "Extracting Shapley values for Ethiopia\n",
      "  Extracted Shapley values from wave 4.0: {'prod': np.float32(0.24567801), 'inc': np.float32(0.21784966), 'asset': np.float32(0.15033828), 'cred': np.float32(0.08618955), 'time': np.float32(0.21375504)}\n",
      "\n",
      "Extracting Shapley values for Malawi\n",
      "  Extracted Shapley values from wave 4.0: {'prod': np.float32(0.21685751), 'inc': np.float32(0.24407926), 'asset': np.float32(0.16070834), 'cred': np.float32(0.086614706), 'time': np.float32(0.20512559)}\n",
      "\n",
      "Extracting Shapley values for Tanzania\n",
      "  Extracted Shapley values from wave 5.0: {'prod': np.float32(0.2217386), 'inc': np.float32(0.24601243), 'asset': np.float32(0.1536963), 'cred': np.float32(0.08436257), 'time': np.float32(0.2098275)}\n",
      "\n",
      "Extracting Shapley values for Uganda\n",
      "  Extracted Shapley values from wave 5.0: {'prod': np.float32(0.26683772), 'inc': np.float32(0.29634377), 'asset': np.float32(0.18986529), 'cred': 0.0, 'time': np.float32(0.24695319)}\n",
      "\n",
      "Extracting Shapley values for Nigeria\n",
      "  Extracted Shapley values from wave 4.0: {'prod': np.float32(0.22556555), 'inc': np.float32(0.25184977), 'asset': np.float32(0.14964919), 'cred': np.float32(0.083162926), 'time': np.float32(0.20660964)}\n",
      "\n",
      "Creating Shapley decomposition visualizations...\n",
      "\n",
      "==================================================\n",
      "Analyzing component trends over time...\n",
      "\n",
      "==================================================\n",
      "Creating component t-test tables...\n",
      "\n",
      "Processing component t-tests for Ethiopia_Malawi\n",
      "Created component t-test table for Ethiopia_Malawi\n",
      "\n",
      "Processing component t-tests for Tanzania_Uganda\n",
      "Created component t-test table for Tanzania_Uganda\n",
      "\n",
      "Processing component t-tests for Nigeria\n",
      "Created component t-test table for Nigeria\n",
      "\n",
      "==================================================\n",
      "Creating component empowerment percentage table...\n",
      "\n",
      "==================================================\n",
      "Creating visualizations...\n",
      "\n",
      "==================================================\n",
      "Writing results to Excel...\n",
      "- Wrote Empowerment Classification sheet\n",
      "- Wrote Empowerment Means sheet\n",
      "- Wrote Component Summary sheet\n",
      "- Wrote T-Test Ethiopia_Malawi sheet\n",
      "- Wrote T-Test Tanzania_Uganda sheet\n",
      "- Wrote T-Test Nigeria sheet\n",
      "- Wrote Component T-Test Ethiopia_Malawi sheet\n",
      "- Wrote Component T-Test Tanzania_Uganda sheet\n",
      "- Wrote Component T-Test Nigeria sheet\n",
      "- Wrote Component Empowerment Percentages sheet\n",
      "- Wrote Component Trends Ethiopia sheet\n",
      "- Wrote Component Trends Malawi sheet\n",
      "- Wrote Component Trends Tanzania sheet\n",
      "- Wrote Component Trends Uganda sheet\n",
      "- Wrote Component Trends Nigeria sheet\n",
      "- Wrote Summary sheet\n",
      "- Wrote Shapley Decomposition sheet\n",
      "- Wrote Cash Crop Decisions sheet\n",
      "- Wrote Cash Crop Wave Summary sheet\n",
      "\n",
      "Analysis completed successfully. Results saved to C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\A-WEAI_Analysis_Results.xlsx\n",
      "Visualizations saved to C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\figures\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "import traceback\n",
    "import time\n",
    "\n",
    "# Define file paths\n",
    "data_paths = {\n",
    "    'Ethiopia': r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\EthPanel-5.dta\",\n",
    "    'Malawi': r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\MalPanel-5.dta\",\n",
    "    'Tanzania': r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\TanPanel-5.dta\",\n",
    "    'Uganda': r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\UgaPanel-6.dta\",\n",
    "    'Nigeria': r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\NigPanel-6.dta\"\n",
    "}\n",
    "\n",
    "# Define country codes and order\n",
    "country_codes = {\n",
    "    'Ethiopia': 'ETH',\n",
    "    'Malawi': 'MLW',\n",
    "    'Tanzania': 'TZN',\n",
    "    'Uganda': 'UGD',\n",
    "    'Nigeria': 'NGR'\n",
    "}\n",
    "\n",
    "# Define the order of countries for output\n",
    "country_order = ['Ethiopia', 'Malawi', 'Tanzania', 'Uganda', 'Nigeria']\n",
    "\n",
    "# Define output directory\n",
    "output_dir = r\"C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\"\n",
    "output_path = os.path.join(output_dir, \"A-WEAI_Analysis_Results.xlsx\")\n",
    "\n",
    "# Ensure output directory exists\n",
    "os.makedirs(output_dir, exist_ok=True)\n",
    "print(f\"Output directory: {output_dir}\")\n",
    "\n",
    "# Create figures directory\n",
    "fig_dir = os.path.join(output_dir, \"figures\")\n",
    "os.makedirs(fig_dir, exist_ok=True)\n",
    "print(f\"Figures directory: {fig_dir}\")\n",
    "\n",
    "# Define component weights for A-WEAI\n",
    "def get_component_weight(component_name):\n",
    "    \"\"\"Return the correct weight for each A-WEAI component based on variable name\"\"\"\n",
    "    if any(x in component_name for x in ['prod', 'inc', 'time']):\n",
    "        return 0.25  # Production, income, and time domains: 25% each\n",
    "    elif 'asset' in component_name and ('loan' not in component_name and 'cred' not in component_name):\n",
    "        return 1/6  # Asset domain: 1/6 or ~16.7%\n",
    "    elif any(x in component_name for x in ['loan', 'cred']):\n",
    "        return 1/12  # Credit domain: 1/12 or ~8.3%\n",
    "    else:\n",
    "        return 0.20  # Default weight\n",
    "\n",
    "# Define classification functions\n",
    "def classify_mixed_empowerment(seq):\n",
    "    \"\"\"\n",
    "    Classifies households with mixed empowerment patterns:\n",
    "    - Became Empowered: If starts with 0, then at first occurrence of 1, \n",
    "      all subsequent rounds are 1, and there are at least 2 rounds of empowerment.\n",
    "    - Became Disempowered: If starts with 1 and final rounds (at least 2) are 0.\n",
    "    - Fluctuating: Otherwise.\n",
    "    \"\"\"\n",
    "    # Special case for only 2 waves\n",
    "    if len(seq) == 2:\n",
    "        if seq[0] == 0 and seq[1] == 1:\n",
    "            return \"became_empowered\"\n",
    "        elif seq[0] == 1 and seq[1] == 0:\n",
    "            return \"became_disempowered\"\n",
    "        else:\n",
    "            return \"fluctuating\"\n",
    "    \n",
    "    # Regular case with more than 2 waves\n",
    "    if seq[0] == 0:\n",
    "        try:\n",
    "            first_empowered = seq.index(1)\n",
    "        except ValueError:\n",
    "            first_empowered = len(seq)\n",
    "        if all(x == 1 for x in seq[first_empowered:]) and (len(seq) - first_empowered >= 2):\n",
    "            return \"became_empowered\"\n",
    "    if seq[0] == 1:\n",
    "        reversed_seq = seq[::-1]\n",
    "        count_zeros = 0\n",
    "        for val in reversed_seq:\n",
    "            if val == 0:\n",
    "                count_zeros += 1\n",
    "            else:\n",
    "                break\n",
    "        if count_zeros >= 2:\n",
    "            return \"became_disempowered\"\n",
    "    return \"fluctuating\"\n",
    "\n",
    "def classify_mixed_empowerment_relaxed(seq):\n",
    "    \"\"\"\n",
    "    Relaxed classification for households with mixed empowerment patterns:\n",
    "    - Became Empowered: If starts with 0 and then from first occurrence of 1 \n",
    "      all subsequent rounds are 1, regardless of the number of rounds.\n",
    "    - Became Disempowered: If starts with 1 and then from first occurrence of 0 \n",
    "      all subsequent rounds are 0.\n",
    "    - Fluctuating: Otherwise.\n",
    "    \"\"\"\n",
    "    # Special case for only 2 waves\n",
    "    if len(seq) == 2:\n",
    "        if seq[0] == 0 and seq[1] == 1:\n",
    "            return \"became_empowered_relaxed\"\n",
    "        elif seq[0] == 1 and seq[1] == 0:\n",
    "            return \"became_disempowered_relaxed\"\n",
    "        else:\n",
    "            return \"fluctuating_relaxed\"\n",
    "    \n",
    "    # Regular case with more than 2 waves\n",
    "    if seq[0] == 0:\n",
    "        try:\n",
    "            first_empowered = seq.index(1)\n",
    "        except ValueError:\n",
    "            first_empowered = len(seq)\n",
    "        if all(x == 1 for x in seq[first_empowered:]):\n",
    "            return \"became_empowered_relaxed\"\n",
    "    if seq[0] == 1:\n",
    "        try:\n",
    "            first_disempowered = seq.index(0)\n",
    "        except ValueError:\n",
    "            first_disempowered = len(seq)\n",
    "        if all(x == 0 for x in seq[first_disempowered:]):\n",
    "            return \"became_disempowered_relaxed\"\n",
    "    return \"fluctuating_relaxed\"\n",
    "\n",
    "# Function to standardize component variables\n",
    "def standardize_component_names(components_list):\n",
    "    \"\"\"Standardize component variable names to avoid duplicates like aweai_asset and aweai_asset2\"\"\"\n",
    "    standard_components = {}\n",
    "    \n",
    "    # Define the component types we're looking for\n",
    "    component_types = ['prod', 'inc', 'asset', 'cred', 'loan', 'time']\n",
    "    \n",
    "    # For each component type, find the corresponding variable\n",
    "    for comp_type in component_types:\n",
    "        # First, try exact match with aweai_ prefix (no number)\n",
    "        for var in components_list:\n",
    "            if f'aweai_{comp_type}' == var:\n",
    "                standard_components[comp_type] = var\n",
    "                break\n",
    "        \n",
    "        # If not found, try with number suffix\n",
    "        if comp_type not in standard_components:\n",
    "            for var in components_list:\n",
    "                if f'aweai_{comp_type}2' == var:\n",
    "                    standard_components[comp_type] = var\n",
    "                    break\n",
    "        \n",
    "        # If still not found, try alternative naming patterns\n",
    "        if comp_type not in standard_components:\n",
    "            for var in components_list:\n",
    "                if comp_type in var:\n",
    "                    standard_components[comp_type] = var\n",
    "                    break\n",
    "    \n",
    "    # Special case: treat 'loan' and 'cred' as the same component\n",
    "    if 'loan' in standard_components and 'cred' not in standard_components:\n",
    "        standard_components['cred'] = standard_components['loan']\n",
    "    elif 'cred' in standard_components and 'loan' not in standard_components:\n",
    "        standard_components['loan'] = standard_components['cred']\n",
    "    \n",
    "    return standard_components\n",
    "\n",
    "# Function to perform t-test between empowered and disempowered households\n",
    "def perform_ttest(df, var_name, group_var='emp'):\n",
    "    \"\"\"Perform t-test between empowered and disempowered groups for a variable\"\"\"\n",
    "    try:\n",
    "        # Check if variable exists\n",
    "        if var_name not in df.columns:\n",
    "            return {'exists': False}\n",
    "        \n",
    "        # Get groups\n",
    "        group0 = df[df[group_var] == 0][var_name].dropna()\n",
    "        group1 = df[df[group_var] == 1][var_name].dropna()\n",
    "        \n",
    "        # Check if enough data\n",
    "        if len(group0) < 2 or len(group1) < 2:\n",
    "            return {\n",
    "                'exists': True,\n",
    "                'n0': len(group0),\n",
    "                'n1': len(group1),\n",
    "                'mean0': group0.mean() if len(group0) > 0 else np.nan,\n",
    "                'mean1': group1.mean() if len(group1) > 0 else np.nan,\n",
    "                'se0': group0.std() / np.sqrt(len(group0)) if len(group0) > 0 else np.nan,\n",
    "                'se1': group1.std() / np.sqrt(len(group1)) if len(group1) > 0 else np.nan,\n",
    "                'p_value': np.nan,\n",
    "                'significant': False\n",
    "            }\n",
    "        \n",
    "        # Perform t-test (Welch's t-test, not assuming equal variances)\n",
    "        t_stat, p_value = stats.ttest_ind(group0, group1, equal_var=False)\n",
    "        \n",
    "        return {\n",
    "            'exists': True,\n",
    "            'n0': len(group0),\n",
    "            'n1': len(group1),\n",
    "            'mean0': group0.mean(),\n",
    "            'mean1': group1.mean(),\n",
    "            'se0': group0.std() / np.sqrt(len(group0)),\n",
    "            'se1': group1.std() / np.sqrt(len(group1)),\n",
    "            't_stat': t_stat,\n",
    "            'p_value': p_value,\n",
    "            'significant': p_value < 0.05\n",
    "        }\n",
    "    except Exception as e:\n",
    "        print(f\"Error performing t-test on {var_name}: {e}\")\n",
    "        return {'exists': False, 'error': str(e)}\n",
    "\n",
    "# Function to create improved academic t-test tables\n",
    "def create_academic_ttest_tables(country_data, t_test_vars, country_groups, variable_categories):\n",
    "    \"\"\"Creates improved academic t-test tables with stars next to significant values\"\"\"\n",
    "    academic_ttest_tables = {}\n",
    "    \n",
    "    for group_name, countries in country_groups.items():\n",
    "        print(f\"\\nProcessing t-tests for {group_name}\")\n",
    "        \n",
    "        # Create rows by variable\n",
    "        rows = []\n",
    "        \n",
    "        # Process each category\n",
    "        for category, variables in variable_categories.items():\n",
    "            # Add category header\n",
    "            category_row = {'Variable': f\"**{category}**\"}\n",
    "            for country in countries:\n",
    "                category_row[f'{country} Disempowered'] = \"\"\n",
    "                category_row[f'{country} Empowered'] = \"\"\n",
    "            rows.append(category_row)\n",
    "            \n",
    "            # Process each variable in this category\n",
    "            for var, var_display_name in variables:\n",
    "                if var in t_test_vars:\n",
    "                    row = {'Variable': f\"**{var_display_name}**\"}\n",
    "                    \n",
    "                    # Process each country in this group\n",
    "                    for country in countries:\n",
    "                        if country in country_data:\n",
    "                            df = country_data[country]\n",
    "                            \n",
    "                            # Perform t-test\n",
    "                            result = perform_ttest(df, var)\n",
    "                            \n",
    "                            if result.get('exists', False):\n",
    "                                # Extract values\n",
    "                                mean_disemp = result.get('mean0', np.nan)\n",
    "                                se_disemp = result.get('se0', np.nan)\n",
    "                                mean_emp = result.get('mean1', np.nan)\n",
    "                                se_emp = result.get('se1', np.nan)\n",
    "                                p_value = result.get('p_value', np.nan)\n",
    "                                \n",
    "                                # Format for academic presentation with significance stars\n",
    "                                row[f'{country} Disempowered'] = f\"{mean_disemp:.3f} ({se_disemp:.3f})\"\n",
    "                                \n",
    "                                # Add stars directly to empowered mean value\n",
    "                                stars = \"\"\n",
    "                                if pd.notnull(p_value):\n",
    "                                    if p_value < 0.01:\n",
    "                                        stars = \"***\"\n",
    "                                    elif p_value < 0.05:\n",
    "                                        stars = \"**\"\n",
    "                                    elif p_value < 0.1:\n",
    "                                        stars = \"*\"\n",
    "                                \n",
    "                                row[f'{country} Empowered'] = f\"{mean_emp:.3f}{stars} ({se_emp:.3f})\"\n",
    "                            else:\n",
    "                                # Variable not available\n",
    "                                row[f'{country} Disempowered'] = \"\"\n",
    "                                row[f'{country} Empowered'] = \"\"\n",
    "                        else:\n",
    "                            # Country data not available\n",
    "                            row[f'{country} Disempowered'] = \"\"\n",
    "                            row[f'{country} Empowered'] = \"\"\n",
    "                    \n",
    "                    rows.append(row)\n",
    "        \n",
    "        # Create table\n",
    "        if rows:\n",
    "            table = pd.DataFrame(rows)\n",
    "            \n",
    "            # Add a row explaining significance levels\n",
    "            note_row = {'Variable': 'Note: *** p<0.01, ** p<0.05, * p<0.1 indicate significant differences between empowered and disempowered groups.'}\n",
    "            for col in table.columns:\n",
    "                if col != 'Variable':\n",
    "                    note_row[col] = ''\n",
    "            \n",
    "            table = pd.concat([table, pd.DataFrame([note_row])], ignore_index=True)\n",
    "            academic_ttest_tables[group_name] = table\n",
    "            \n",
    "            print(f\"Created academic table for {group_name}\")\n",
    "    \n",
    "    return academic_ttest_tables\n",
    "\n",
    "# Function to extract Shapley decomposition from component contributions\n",
    "def extract_shapley_from_components(results, country_order):\n",
    "    \"\"\"\n",
    "    Extracts Shapley-like values from the already calculated component contributions to disempowerment\n",
    "    \"\"\"\n",
    "    # Dictionary to store results\n",
    "    shapley_results = {}\n",
    "    \n",
    "    # Component types\n",
    "    all_component_types = ['prod', 'inc', 'asset', 'cred', 'time']\n",
    "    \n",
    "    for country in country_order:\n",
    "        if country in results['empowerment_components']:\n",
    "            print(f\"\\nExtracting Shapley values for {country}\")\n",
    "            \n",
    "            component_data = results['empowerment_components'][country]\n",
    "            \n",
    "            # Get the most recent wave's contributions\n",
    "            waves = sorted(component_data['by_status'].keys())\n",
    "            if waves:\n",
    "                last_wave = waves[-1]\n",
    "                status_data = component_data['by_status'][last_wave]\n",
    "                \n",
    "                if 'contributions' in status_data:\n",
    "                    contributions = status_data['contributions']\n",
    "                    \n",
    "                    # Get values for all components\n",
    "                    shapley_values = {}\n",
    "                    for comp_type in all_component_types:\n",
    "                        if comp_type in contributions:\n",
    "                            # Convert percentage to proportion\n",
    "                            shapley_values[comp_type] = contributions[comp_type] / 100.0\n",
    "                        else:\n",
    "                            shapley_values[comp_type] = 0.0\n",
    "                    \n",
    "                    # Store results\n",
    "                    shapley_results[country] = shapley_values\n",
    "                    print(f\"  Extracted Shapley values from wave {last_wave}: {shapley_values}\")\n",
    "                else:\n",
    "                    print(f\"  No contribution data found for {country} in wave {last_wave}\")\n",
    "            else:\n",
    "                print(f\"  No wave data found for {country}\")\n",
    "        else:\n",
    "            print(f\"  No component data found for {country}\")\n",
    "    \n",
    "    return shapley_results\n",
    "\n",
    "# Function to create bar charts for Shapley decomposition\n",
    "def create_shapley_charts(shapley_results, country_order, fig_dir):\n",
    "    \"\"\"Creates bar charts showing Shapley value decomposition by country\"\"\"\n",
    "    # Component types\n",
    "    all_component_types = ['prod', 'inc', 'asset', 'cred', 'time']\n",
    "    \n",
    "    # Component colors (consistent with previous charts)\n",
    "    component_colors = {\n",
    "        'prod': '#1f77b4',  # Blue\n",
    "        'inc': '#ff7f0e',   # Orange\n",
    "        'asset': '#2ca02c', # Green\n",
    "        'cred': '#d62728',  # Red\n",
    "        'time': '#9467bd'   # Purple\n",
    "    }\n",
    "    \n",
    "    # Component labels for nicer display\n",
    "    component_labels = {\n",
    "        'prod': 'Production',\n",
    "        'inc': 'Income',\n",
    "        'asset': 'Asset Ownership',\n",
    "        'cred': 'Credit Access',\n",
    "        'time': 'Time Use'\n",
    "    }\n",
    "    \n",
    "    # 1. Individual country charts\n",
    "    for country in country_order:\n",
    "        if country in shapley_results:\n",
    "            values = shapley_results[country]\n",
    "            \n",
    "            # Create sorted data for the chart\n",
    "            components = []\n",
    "            importance = []\n",
    "            colors = []\n",
    "            \n",
    "            # Sort by importance\n",
    "            for comp_type, value in sorted(values.items(), key=lambda x: x[1], reverse=True):\n",
    "                components.append(component_labels.get(comp_type, comp_type))\n",
    "                importance.append(value * 100)  # Convert to percentage\n",
    "                colors.append(component_colors.get(comp_type, '#333333'))\n",
    "            \n",
    "            # Create chart\n",
    "            plt.figure(figsize=(10, 6))\n",
    "            bars = plt.barh(components, importance, color=colors)\n",
    "            \n",
    "            # Add value labels\n",
    "            for bar in bars:\n",
    "                width = bar.get_width()\n",
    "                plt.text(width + 1, bar.get_y() + bar.get_height()/2, \n",
    "                         f'{width:.1f}%', ha='left', va='center')\n",
    "            \n",
    "            plt.xlabel('Relative Importance (%)')\n",
    "            plt.title(f'Contribution to Disempowerment - {country}')\n",
    "            plt.grid(axis='x', alpha=0.3)\n",
    "            plt.tight_layout()\n",
    "            \n",
    "            # Save figure\n",
    "            plt.savefig(os.path.join(fig_dir, f'shapley_{country}.png'), dpi=300)\n",
    "            plt.savefig(os.path.join(fig_dir, f'shapley_{country}.pdf'), format='pdf')\n",
    "            plt.close()\n",
    "    \n",
    "    # 2. Combined chart for all countries\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    \n",
    "    # Get countries with results\n",
    "    valid_countries = [c for c in country_order if c in shapley_results]\n",
    "    \n",
    "    if valid_countries:\n",
    "        # Set up positions\n",
    "        x = np.arange(len(valid_countries))\n",
    "        width = 0.15\n",
    "        offsets = np.linspace(-(width * 2), (width * 2), len(all_component_types))\n",
    "        \n",
    "        # Plot each component as a group\n",
    "        for i, comp_type in enumerate(all_component_types):\n",
    "            values = [shapley_results[country].get(comp_type, 0) * 100 for country in valid_countries]\n",
    "            bars = plt.bar(x + offsets[i], values, width, \n",
    "                          label=component_labels.get(comp_type, comp_type),\n",
    "                          color=component_colors.get(comp_type, '#333333'))\n",
    "            \n",
    "            # Add value labels for significant contributions\n",
    "            for j, bar in enumerate(bars):\n",
    "                height = bar.get_height()\n",
    "                if height > 10:  # Only label if contribution is >10%\n",
    "                    plt.text(bar.get_x() + bar.get_width()/2, height + 1,\n",
    "                             f'{height:.0f}%', ha='center', va='bottom', \n",
    "                             fontsize=8, rotation=0)\n",
    "        \n",
    "        plt.xlabel('Country')\n",
    "        plt.ylabel('Relative Importance (%)')\n",
    "        plt.title('Contribution to Disempowerment by Dimension and Country')\n",
    "        plt.xticks(x, valid_countries)\n",
    "        plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=5)\n",
    "        plt.grid(axis='y', alpha=0.3)\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        # Save combined figure\n",
    "        plt.savefig(os.path.join(fig_dir, 'shapley_combined.png'), dpi=300)\n",
    "        plt.savefig(os.path.join(fig_dir, 'shapley_combined.pdf'), format='pdf')\n",
    "        plt.close()\n",
    "\n",
    "# Function to analyze component trends over time with improved scaling and formatting\n",
    "def analyze_component_trends(results, country_order, fig_dir, colors):\n",
    "    \"\"\"Analyzes and visualizes trends in component values over time for each country\"\"\"\n",
    "    all_component_types = ['prod', 'inc', 'asset', 'cred', 'time']\n",
    "    component_labels = {\n",
    "        'prod': 'Production',\n",
    "        'inc': 'Income',\n",
    "        'asset': 'Asset Ownership',\n",
    "        'cred': 'Credit Access',\n",
    "        'time': 'Time Use'\n",
    "    }\n",
    "    \n",
    "    # Component colors (consistent with previous charts)\n",
    "    component_colors = {\n",
    "        'prod': '#1f77b4',  # Blue\n",
    "        'inc': '#ff7f0e',   # Orange\n",
    "        'asset': '#2ca02c', # Green\n",
    "        'cred': '#d62728',  # Red\n",
    "        'time': '#9467bd'   # Purple\n",
    "    }\n",
    "    \n",
    "    # Create a table for component trends\n",
    "    component_trend_tables = {}\n",
    "    \n",
    "    # Process each country\n",
    "    for country in country_order:\n",
    "        if country in results['empowerment_components']:\n",
    "            component_data = results['empowerment_components'][country]\n",
    "            \n",
    "            if 'by_wave' in component_data:\n",
    "                wave_data = component_data['by_wave']\n",
    "                waves = sorted(wave_data.keys())\n",
    "                \n",
    "                if len(waves) > 1:\n",
    "                    # Create a DataFrame to store the trend data\n",
    "                    trend_df = pd.DataFrame(index=waves)\n",
    "                    \n",
    "                    # Create a plot for this country\n",
    "                    plt.figure(figsize=(10, 6))\n",
    "                    \n",
    "                    # Find min and max values for all components to set y-axis limits\n",
    "                    all_values = []\n",
    "                    \n",
    "                    # First pass to collect all values for scaling\n",
    "                    for comp_type in all_component_types:\n",
    "                        for wave in waves:\n",
    "                            if comp_type in wave_data[wave]:\n",
    "                                all_values.append(wave_data[wave][comp_type])\n",
    "                    \n",
    "                    # Calculate y-axis limits with padding\n",
    "                    if all_values:\n",
    "                        min_val = min(all_values)\n",
    "                        max_val = max(all_values)\n",
    "                        padding = (max_val - min_val) * 0.1 if max_val > min_val else 0.05\n",
    "                        y_min = max(0, min_val - padding)  # Don't go below 0\n",
    "                        y_max = max_val + padding\n",
    "                    else:\n",
    "                        y_min, y_max = 0, 1\n",
    "                    \n",
    "                    # Add each component to the plot and DataFrame\n",
    "                    for comp_type in all_component_types:\n",
    "                        # Collect values across waves\n",
    "                        values = []\n",
    "                        valid_waves = []\n",
    "                        \n",
    "                        for wave in waves:\n",
    "                            if comp_type in wave_data[wave]:\n",
    "                                value = wave_data[wave][comp_type]\n",
    "                                values.append(value)\n",
    "                                valid_waves.append(wave)\n",
    "                                # Add to DataFrame\n",
    "                                trend_df.at[wave, comp_type] = value\n",
    "                            else:\n",
    "                                # Add NaN to DataFrame\n",
    "                                trend_df.at[wave, comp_type] = np.nan\n",
    "                        \n",
    "                        # Plot if we have data\n",
    "                        if values:\n",
    "                            plt.plot(valid_waves, values, marker='o', linewidth=2, \n",
    "                                     label=component_labels.get(comp_type, comp_type),\n",
    "                                     color=component_colors.get(comp_type, '#333333'))\n",
    "                            \n",
    "                            # Add value labels with slight offsets to prevent overlapping\n",
    "                            for i, (wave, value) in enumerate(zip(valid_waves, values)):\n",
    "                                # Alternate label positions slightly up/down to avoid overlaps\n",
    "                                offset = 0.01 + (i % 2) * 0.01\n",
    "                                plt.text(wave, value + offset, f'{value:.2f}', \n",
    "                                         ha='center', va='bottom', fontsize=8,\n",
    "                                         color=component_colors.get(comp_type, '#333333'),\n",
    "                                         bbox=dict(facecolor='white', alpha=0.7, pad=1, edgecolor='none'))\n",
    "                    \n",
    "                    # Finalize plot\n",
    "                    plt.xlabel('Wave')\n",
    "                    plt.ylabel('Component Score')\n",
    "                    plt.title(f'A-WEAI Component Trends - {country}')\n",
    "                    plt.grid(True, linestyle='--', alpha=0.7)\n",
    "                    plt.legend(loc='best')\n",
    "                    \n",
    "                    # Set y-axis limits based on data range\n",
    "                    plt.ylim(y_min, y_max)\n",
    "                    \n",
    "                    # Set x-axis ticks to integer waves only\n",
    "                    integer_waves = [int(wave) for wave in waves if wave.is_integer()]\n",
    "                    plt.xticks(integer_waves)\n",
    "                    \n",
    "                    plt.tight_layout()\n",
    "                    plt.savefig(os.path.join(fig_dir, f'component_trends_{country}.png'), dpi=300)\n",
    "                    plt.savefig(os.path.join(fig_dir, f'component_trends_{country}.pdf'), format='pdf')\n",
    "                    plt.close()\n",
    "                    \n",
    "                    # Store the trend DataFrame\n",
    "                    component_trend_tables[country] = trend_df\n",
    "    \n",
    "    # Create a combined plot for all countries by component\n",
    "    for comp_type in all_component_types:\n",
    "        plt.figure(figsize=(10, 6))\n",
    "        \n",
    "        # First pass to collect all values for this component across countries\n",
    "        all_values = []\n",
    "        for country in country_order:\n",
    "            if country in component_trend_tables:\n",
    "                trend_df = component_trend_tables[country]\n",
    "                \n",
    "                if comp_type in trend_df.columns:\n",
    "                    # Extract non-NaN values\n",
    "                    values = trend_df[comp_type].dropna()\n",
    "                    if not values.empty:\n",
    "                        all_values.extend(values)\n",
    "        \n",
    "        # Calculate y-axis limits with padding\n",
    "        if all_values:\n",
    "            min_val = min(all_values)\n",
    "            max_val = max(all_values)\n",
    "            padding = (max_val - min_val) * 0.1 if max_val > min_val else 0.05\n",
    "            y_min = max(0, min_val - padding)  # Don't go below 0\n",
    "            y_max = max_val + padding\n",
    "        else:\n",
    "            y_min, y_max = 0, 1\n",
    "        \n",
    "        # Second pass to plot data\n",
    "        # Collect all integer waves seen across countries for x-axis\n",
    "        all_integer_waves = set()\n",
    "        \n",
    "        for i, country in enumerate(country_order):\n",
    "            if country in component_trend_tables:\n",
    "                trend_df = component_trend_tables[country]\n",
    "                \n",
    "                if comp_type in trend_df.columns:\n",
    "                    # Extract non-NaN values and corresponding waves\n",
    "                    values = trend_df[comp_type].dropna()\n",
    "                    \n",
    "                    if not values.empty:\n",
    "                        waves = values.index\n",
    "                        # Collect integer waves for x-axis\n",
    "                        all_integer_waves.update([int(w) for w in waves if float(w).is_integer()])\n",
    "                        \n",
    "                        # Plot the data\n",
    "                        plt.plot(waves, values, marker='o', linewidth=2, \n",
    "                                 label=country, color=colors[i % len(colors)])\n",
    "                        \n",
    "                        # Add value labels with slight offsets to prevent overlapping\n",
    "                        for j, (wave, value) in enumerate(zip(waves, values)):\n",
    "                            # Create vertical offset based on country and position\n",
    "                            offset = 0.01 + (i * 0.005) + (j % 2) * 0.01\n",
    "                            plt.text(wave, value + offset, f'{value:.2f}', \n",
    "                                     ha='center', va='bottom', fontsize=8,\n",
    "                                     color=colors[i % len(colors)],\n",
    "                                     bbox=dict(facecolor='white', alpha=0.7, pad=1, edgecolor='none'))\n",
    "        \n",
    "        # Finalize plot\n",
    "        plt.xlabel('Wave')\n",
    "        plt.ylabel('Component Score')\n",
    "        plt.title(f'A-WEAI {component_labels.get(comp_type, comp_type)} Trends Across Countries')\n",
    "        plt.grid(True, linestyle='--', alpha=0.7)\n",
    "        plt.legend(loc='best')\n",
    "        \n",
    "        # Set y-axis limits based on data range\n",
    "        plt.ylim(y_min, y_max)\n",
    "        \n",
    "        # Set x-axis ticks to integer waves only\n",
    "        plt.xticks(sorted(all_integer_waves))\n",
    "        \n",
    "        plt.tight_layout()\n",
    "        plt.savefig(os.path.join(fig_dir, f'component_trends_{comp_type}.png'), dpi=300)\n",
    "        plt.savefig(os.path.join(fig_dir, f'component_trends_{comp_type}.pdf'), format='pdf')\n",
    "        plt.close()\n",
    "    \n",
    "    return component_trend_tables\n",
    "# Function to create t-test tables for comparing components between empowered and disempowered groups\n",
    "def create_component_ttest_tables(results, country_groups):\n",
    "    \"\"\"Creates t-test tables comparing component values between empowered and disempowered groups\"\"\"\n",
    "    component_ttest_tables = {}\n",
    "    \n",
    "    # Component types with proper names\n",
    "    component_types = [\n",
    "        ('prod', 'Production'),\n",
    "        ('inc', 'Income'),\n",
    "        ('asset', 'Asset Ownership'),\n",
    "        ('cred', 'Credit Access'),\n",
    "        ('time', 'Time Use')\n",
    "    ]\n",
    "    \n",
    "    for group_name, countries in country_groups.items():\n",
    "        print(f\"\\nProcessing component t-tests for {group_name}\")\n",
    "        \n",
    "        # Create rows by component\n",
    "        rows = []\n",
    "        \n",
    "        # Add header row\n",
    "        header_row = {'Component': '**Empowerment Dimensions**'}\n",
    "        for country in countries:\n",
    "            header_row[f'{country} Disempowered'] = \"\"\n",
    "            header_row[f'{country} Empowered'] = \"\"\n",
    "        rows.append(header_row)\n",
    "        \n",
    "        # Add rows for each component\n",
    "        for comp_code, comp_name in component_types:\n",
    "            row = {'Component': f\"**{comp_name}**\"}\n",
    "            \n",
    "            # Process each country in this group\n",
    "            for country in countries:\n",
    "                if country in results['country_data'] and country in results['standardized_components']:\n",
    "                    df = results['country_data'][country]\n",
    "                    \n",
    "                    # Get the standardized component variable name\n",
    "                    std_components = results['standardized_components'][country]\n",
    "                    if comp_code in std_components:\n",
    "                        var_name = std_components[comp_code]\n",
    "                        \n",
    "                        # Perform t-test\n",
    "                        result = perform_ttest(df, var_name)\n",
    "                        \n",
    "                        if result.get('exists', False):\n",
    "                            # Extract values\n",
    "                            mean_disemp = result.get('mean0', np.nan)\n",
    "                            se_disemp = result.get('se0', np.nan)\n",
    "                            mean_emp = result.get('mean1', np.nan)\n",
    "                            se_emp = result.get('se1', np.nan)\n",
    "                            p_value = result.get('p_value', np.nan)\n",
    "                            \n",
    "                            # Format for academic presentation with significance stars\n",
    "                            row[f'{country} Disempowered'] = f\"{mean_disemp:.3f} ({se_disemp:.3f})\"\n",
    "                            \n",
    "                            # Add stars directly to empowered mean value\n",
    "                            stars = \"\"\n",
    "                            if pd.notnull(p_value):\n",
    "                                if p_value < 0.01:\n",
    "                                    stars = \"***\"\n",
    "                                elif p_value < 0.05:\n",
    "                                    stars = \"**\"\n",
    "                                elif p_value < 0.1:\n",
    "                                    stars = \"*\"\n",
    "                            \n",
    "                            row[f'{country} Empowered'] = f\"{mean_emp:.3f}{stars} ({se_emp:.3f})\"\n",
    "                        else:\n",
    "                            # Variable not available\n",
    "                            row[f'{country} Disempowered'] = \"\"\n",
    "                            row[f'{country} Empowered'] = \"\"\n",
    "                    else:\n",
    "                        # Component not found\n",
    "                        row[f'{country} Disempowered'] = \"\"\n",
    "                        row[f'{country} Empowered'] = \"\"\n",
    "                else:\n",
    "                    # Country data not available\n",
    "                    row[f'{country} Disempowered'] = \"\"\n",
    "                    row[f'{country} Empowered'] = \"\"\n",
    "            \n",
    "            rows.append(row)\n",
    "        \n",
    "        # Create table\n",
    "        if rows:\n",
    "            table = pd.DataFrame(rows)\n",
    "            \n",
    "            # Add a row explaining significance levels\n",
    "            note_row = {'Component': 'Note: *** p<0.01, ** p<0.05, * p<0.1 indicate significant differences between empowered and disempowered groups.'}\n",
    "            for col in table.columns:\n",
    "                if col != 'Component':\n",
    "                    note_row[col] = ''\n",
    "            \n",
    "            table = pd.concat([table, pd.DataFrame([note_row])], ignore_index=True)\n",
    "            component_ttest_tables[group_name] = table\n",
    "            \n",
    "            print(f\"Created component t-test table for {group_name}\")\n",
    "    \n",
    "    return component_ttest_tables\n",
    "\n",
    "# Function to create table with percentage of households empowered in each dimension\n",
    "def create_component_empowerment_table(results, country_order):\n",
    "    \"\"\"Creates a table showing the percentage of households empowered in each dimension (with > 0 values)\"\"\"\n",
    "    all_component_types = ['prod', 'inc', 'asset', 'cred', 'time']\n",
    "    \n",
    "    # Create a DataFrame for results\n",
    "    rows = []\n",
    "    \n",
    "    for country in country_order:\n",
    "        if country in results['country_data'] and country in results['standardized_components']:\n",
    "            df = results['country_data'][country]\n",
    "            std_components = results['standardized_components'][country]\n",
    "            \n",
    "            # Get most recent wave\n",
    "            if country in results['empowerment_means']:\n",
    "                waves = sorted(results['empowerment_means'][country].keys())\n",
    "                if waves:\n",
    "                    last_wave = max(waves)\n",
    "                    wave_data = df[df['t'] == last_wave]\n",
    "                    \n",
    "                    row = {'Country': country}\n",
    "                    \n",
    "                    # Calculate percentage for each component\n",
    "                    for comp_type in all_component_types:\n",
    "                        if comp_type in std_components:\n",
    "                            var_name = std_components[comp_type]\n",
    "                            \n",
    "                            if var_name in wave_data.columns:\n",
    "                                # Count households with positive values\n",
    "                                positive_count = (wave_data[var_name] > 0).sum()\n",
    "                                total_count = wave_data[var_name].notna().sum()\n",
    "                                \n",
    "                                if total_count > 0:\n",
    "                                    percentage = (positive_count / total_count) * 100\n",
    "                                    row[f'{comp_type.capitalize()} (%)'] = percentage\n",
    "                                else:\n",
    "                                    row[f'{comp_type.capitalize()} (%)'] = np.nan\n",
    "                            else:\n",
    "                                row[f'{comp_type.capitalize()} (%)'] = np.nan\n",
    "                        else:\n",
    "                            row[f'{comp_type.capitalize()} (%)'] = np.nan\n",
    "                    \n",
    "                    # Add overall empowerment rate\n",
    "                    if 'emp' in wave_data.columns:\n",
    "                        emp_rate = wave_data['emp'].mean() * 100\n",
    "                        row['Overall (%)'] = emp_rate\n",
    "                    else:\n",
    "                        row['Overall (%)'] = np.nan\n",
    "                    \n",
    "                    rows.append(row)\n",
    "    \n",
    "    # Create DataFrame and ensure countries are in correct order\n",
    "    component_empowerment_df = pd.DataFrame(rows)\n",
    "    if not component_empowerment_df.empty:\n",
    "        # Sort by country order\n",
    "        order_dict = {country: i for i, country in enumerate(country_order)}\n",
    "        component_empowerment_df['sort_key'] = component_empowerment_df['Country'].map(order_dict)\n",
    "        component_empowerment_df = component_empowerment_df.sort_values('sort_key').drop('sort_key', axis=1)\n",
    "    \n",
    "    return component_empowerment_df\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Initialize dictionaries to store results\n",
    "results = {\n",
    "    'empowerment_classification': {},    # Panel classification (always, never, mixed)\n",
    "    'empowerment_means': {},             # Mean empowerment by wave\n",
    "    'empowerment_components': {},        # Component scores by country\n",
    "    'empowerment_changes': {},           # Changes over time\n",
    "    'standardized_components': {},       # Standardized component mapping\n",
    "    'country_data': {}                   # Full country data (to avoid rereading)\n",
    "}\n",
    "\n",
    "debug_info = {}  # Store any errors or issues\n",
    "\n",
    "\n",
    "\n",
    "# Process each country\n",
    "for country_name in country_order:\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"Processing country: {country_name} ({country_codes[country_name]})\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    file_path = data_paths[country_name]\n",
    "    \n",
    "    try:\n",
    "        # Read the data file\n",
    "        df = pd.read_stata(file_path, convert_categoricals=False)\n",
    "        print(f\"Successfully read file. Shape: {df.shape}\")\n",
    "        \n",
    "        # Store country data\n",
    "        results['country_data'][country_name] = df\n",
    "        \n",
    "        # Check for required variables\n",
    "        required_vars = ['id', 't', 'emp']\n",
    "        missing_vars = [var for var in required_vars if var not in df.columns]\n",
    "        if missing_vars:\n",
    "            print(f\"Missing required variables: {missing_vars}\")\n",
    "            debug_info[country_name] = {\"error\": f\"Missing variables: {missing_vars}\"}\n",
    "            continue\n",
    "            \n",
    "        # Find component variables\n",
    "        component_vars = []\n",
    "        for col in df.columns:\n",
    "            if 'aweai_' in col:\n",
    "                component_vars.append(col)\n",
    "                \n",
    "        if component_vars:\n",
    "            print(f\"Found component variables: {component_vars}\")\n",
    "        else:\n",
    "            print(f\"Warning: No component variables found\")\n",
    "        \n",
    "        # Check which waves have valid empowerment data\n",
    "        emp_by_wave = df.groupby('t')['emp'].agg(['count', 'sum', 'mean'])\n",
    "        print(\"\\nEmpowerment data by wave:\")\n",
    "        print(emp_by_wave)\n",
    "        \n",
    "        # Identify waves with no variation in emp (all 0s or NaNs)\n",
    "        invalid_waves = emp_by_wave[(emp_by_wave['sum'] == 0) | (emp_by_wave['count'] == 0)].index.tolist()\n",
    "        if invalid_waves:\n",
    "            print(f\"Warning: Waves with no empowerment data: {invalid_waves}\")\n",
    "            \n",
    "        # Set waves for analysis\n",
    "        valid_waves = sorted([t for t in df['t'].unique() if t not in invalid_waves])\n",
    "        print(f\"Valid waves for analysis: {valid_waves}\")\n",
    "        \n",
    "        # Special handling for Ethiopia - use only waves 2 and 3 for classification\n",
    "        if country_name == 'Ethiopia':\n",
    "            print(f\"\\nSpecial handling for Ethiopia:\")\n",
    "            classification_waves = [2.0, 3.0]\n",
    "            analysis_waves = [2.0, 3.0, 4.0]\n",
    "            \n",
    "            print(f\"  - For classification: Using waves {classification_waves}\")\n",
    "            print(f\"  - For other analyses: Using waves {analysis_waves}\")\n",
    "            \n",
    "            # Filter to only include valid waves\n",
    "            classification_waves = [w for w in classification_waves if w in valid_waves]\n",
    "            analysis_waves = [w for w in analysis_waves if w in valid_waves]\n",
    "        else:\n",
    "            classification_waves = valid_waves\n",
    "            analysis_waves = valid_waves\n",
    "        \n",
    "        # Check if we have enough waves for panel analysis\n",
    "        if len(classification_waves) < 2:\n",
    "            print(f\"Error: Insufficient valid waves for panel analysis (minimum 2 needed)\")\n",
    "            debug_info[country_name] = {\"error\": \"Insufficient valid waves\"}\n",
    "            continue\n",
    "            \n",
    "        # Create filtered datasets for classification and analysis\n",
    "        df_class = df[df['t'].isin(classification_waves)].copy()\n",
    "        print(f\"Classification dataset shape: {df_class.shape}\")\n",
    "        \n",
    "        df_analysis = df[df['t'].isin(analysis_waves)].copy()\n",
    "        print(f\"Analysis dataset shape: {df_analysis.shape}\")\n",
    "        \n",
    "        # Find households with complete data for classification\n",
    "        classification_households = df_class.groupby('id')['t'].nunique()\n",
    "        complete_class_households = classification_households[classification_households == len(classification_waves)].index\n",
    "        print(f\"Households with data in all {len(classification_waves)} classification waves: {len(complete_class_households)}\")\n",
    "        \n",
    "        # Create panel dataset for classification\n",
    "        df_panel_class = df_class[df_class['id'].isin(complete_class_households)].copy()\n",
    "        print(f\"Classification panel shape: {df_panel_class.shape}\")\n",
    "        \n",
    "        # PART 1: Basic Empowerment Classification\n",
    "        print(\"\\nPerforming basic empowerment classification...\")\n",
    "        \n",
    "        # Calculate summary statistics for each household\n",
    "        summary = df_panel_class.groupby('id')['emp'].agg(['min', 'max'])\n",
    "        \n",
    "        # Classify households\n",
    "        always_empowered = (summary['min'] > 0).sum()  # Empowered in every round\n",
    "        never_empowered = (summary['max'] == 0).sum()  # Never empowered\n",
    "        mixed = len(summary) - always_empowered - never_empowered  # Mixed pattern\n",
    "        \n",
    "        print(f\"Basic classification results:\")\n",
    "        print(f\"  Always empowered: {always_empowered} households\")\n",
    "        print(f\"  Never empowered: {never_empowered} households\")\n",
    "        print(f\"  Mixed pattern: {mixed} households\")\n",
    "        \n",
    "        total_households = len(summary)\n",
    "        \n",
    "        # Store classification results\n",
    "        results['empowerment_classification'][country_name] = {\n",
    "            \"Total Households\": total_households,\n",
    "            \"Always Empowered\": always_empowered,\n",
    "            \"Never Empowered\": never_empowered,\n",
    "            \"Mixed\": mixed,\n",
    "            \"Always Empowered (%)\": always_empowered / total_households * 100 if total_households > 0 else np.nan,\n",
    "            \"Never Empowered (%)\": never_empowered / total_households * 100 if total_households > 0 else np.nan,\n",
    "            \"Mixed (%)\": mixed / total_households * 100 if total_households > 0 else np.nan\n",
    "        }\n",
    "        \n",
    "        # PART 2: Detailed Classification of Mixed Households\n",
    "        print(\"\\nPerforming detailed classification of mixed households...\")\n",
    "        \n",
    "        # Group by household (sorted by wave) for classification dataset\n",
    "        grouped = df_panel_class.sort_values('t').groupby('id')\n",
    "        \n",
    "        # Sample a few households to check\n",
    "        sample_households = list(grouped.groups.keys())[:5]\n",
    "        print(f\"Sample households:\")\n",
    "        for hh in sample_households:\n",
    "            hh_data = grouped.get_group(hh).sort_values('t')\n",
    "            print(f\"  ID {hh}: waves={hh_data['t'].tolist()}, emp={hh_data['emp'].tolist()}\")\n",
    "        \n",
    "        # Process all households with mixed patterns\n",
    "        mixed_classifications = {}\n",
    "        mixed_relaxed_classifications = {}\n",
    "        \n",
    "        for hh, group in grouped:\n",
    "            # Sort group by wave and get 'emp' values\n",
    "            seq = list(group.sort_values('t')['emp'])\n",
    "            # Convert to binary (1 if empowered, 0 otherwise)\n",
    "            binary_seq = [1 if (pd.notnull(x) and x > 0) else 0 for x in seq]\n",
    "            \n",
    "            # Only consider households with mixed pattern\n",
    "            if all(x == 0 for x in binary_seq) or all(x == 1 for x in binary_seq):\n",
    "                continue\n",
    "                \n",
    "            # Classify using both methods\n",
    "            mixed_classifications[hh] = classify_mixed_empowerment(binary_seq)\n",
    "            mixed_relaxed_classifications[hh] = classify_mixed_empowerment_relaxed(binary_seq)\n",
    "        \n",
    "        # Count classifications\n",
    "        total_mixed = len(mixed_classifications)\n",
    "        \n",
    "        if total_mixed > 0:\n",
    "            # Standard classification\n",
    "            became_empowered = sum(1 for v in mixed_classifications.values() if v == \"became_empowered\")\n",
    "            became_disempowered = sum(1 for v in mixed_classifications.values() if v == \"became_disempowered\")\n",
    "            fluctuating = sum(1 for v in mixed_classifications.values() if v == \"fluctuating\")\n",
    "            \n",
    "            print(f\"Mixed households classification (standard):\")\n",
    "            print(f\"  Became empowered: {became_empowered} ({became_empowered/total_mixed*100:.1f}%)\")\n",
    "            print(f\"  Became disempowered: {became_disempowered} ({became_disempowered/total_mixed*100:.1f}%)\")\n",
    "            print(f\"  Fluctuating: {fluctuating} ({fluctuating/total_mixed*100:.1f}%)\")\n",
    "            \n",
    "            # Relaxed classification\n",
    "            became_empowered_relaxed = sum(1 for v in mixed_relaxed_classifications.values() if v == \"became_empowered_relaxed\")\n",
    "            became_disempowered_relaxed = sum(1 for v in mixed_relaxed_classifications.values() if v == \"became_disempowered_relaxed\")\n",
    "            fluctuating_relaxed = sum(1 for v in mixed_relaxed_classifications.values() if v == \"fluctuating_relaxed\")\n",
    "            \n",
    "            print(f\"Mixed households classification (relaxed):\")\n",
    "            print(f\"  Became empowered: {became_empowered_relaxed} ({became_empowered_relaxed/total_mixed*100:.1f}%)\")\n",
    "            print(f\"  Became disempowered: {became_disempowered_relaxed} ({became_disempowered_relaxed/total_mixed*100:.1f}%)\")\n",
    "            print(f\"  Fluctuating: {fluctuating_relaxed} ({fluctuating_relaxed/total_mixed*100:.1f}%)\")\n",
    "            \n",
    "            # Add detailed results to classification dictionary\n",
    "            results['empowerment_classification'][country_name].update({\n",
    "                \"Became Empowered\": became_empowered,\n",
    "                \"Became Disempowered\": became_disempowered,\n",
    "                \"Fluctuating\": fluctuating,\n",
    "                \"Became Empowered (%)\": became_empowered / total_mixed * 100,\n",
    "                \"Became Disempowered (%)\": became_disempowered / total_mixed * 100,\n",
    "                \"Fluctuating (%)\": fluctuating / total_mixed * 100,\n",
    "                \"Became Empowered Relaxed\": became_empowered_relaxed,\n",
    "                \"Became Disempowered Relaxed\": became_disempowered_relaxed,\n",
    "                \"Fluctuating Relaxed\": fluctuating_relaxed\n",
    "            })\n",
    "        else:\n",
    "            print(\"No mixed households found for detailed classification\")\n",
    "            \n",
    "        # PART 3: Empowerment Means by Wave\n",
    "        print(\"\\nCalculating empowerment means by wave...\")\n",
    "        \n",
    "        # Use analysis dataset for this\n",
    "        wave_means = {}\n",
    "        \n",
    "        for wave in analysis_waves:\n",
    "            wave_data = df_analysis[df_analysis['t'] == wave]\n",
    "            if len(wave_data) > 0:\n",
    "                mean = wave_data['emp'].mean()\n",
    "                std = wave_data['emp'].std()\n",
    "                count = len(wave_data)\n",
    "                wave_means[wave] = {\n",
    "                    'mean': mean,\n",
    "                    'std': std,\n",
    "                    'count': count\n",
    "                }\n",
    "                print(f\"  Wave {wave}: mean={mean:.4f}, std={std:.4f}, n={count}\")\n",
    "        \n",
    "        # Store wave means\n",
    "        results['empowerment_means'][country_name] = wave_means\n",
    "        \n",
    "        # PART 4: Component Analysis\n",
    "        print(\"\\nAnalyzing empowerment components...\")\n",
    "        \n",
    "        # Standardize component variables\n",
    "        std_components = standardize_component_names(component_vars)\n",
    "        print(f\"Standardized components: {std_components}\")\n",
    "        \n",
    "        # Store standardized components\n",
    "        results['standardized_components'][country_name] = std_components\n",
    "        \n",
    "        # Create dictionary to store component analysis\n",
    "        component_analysis = {\n",
    "            'components': std_components,\n",
    "            'by_wave': {},\n",
    "            'by_status': {}\n",
    "        }\n",
    "        \n",
    "        # Analyze components by wave\n",
    "        for wave in analysis_waves:\n",
    "            wave_data = df_analysis[df_analysis['t'] == wave]\n",
    "            \n",
    "            if len(wave_data) > 0:\n",
    "                # Overall component values for this wave\n",
    "                wave_component_values = {}\n",
    "                for comp_type, var_name in std_components.items():\n",
    "                    if var_name in wave_data.columns:\n",
    "                        wave_component_values[comp_type] = wave_data[var_name].mean()\n",
    "                \n",
    "                # Store overall values\n",
    "                component_analysis['by_wave'][wave] = wave_component_values\n",
    "                \n",
    "                # Analyze by empowerment status\n",
    "                empowered = wave_data[wave_data['emp'] == 1]\n",
    "                disempowered = wave_data[wave_data['emp'] == 0]\n",
    "                \n",
    "                emp_status_values = {}\n",
    "                \n",
    "                # For empowered households\n",
    "                if len(empowered) > 0:\n",
    "                    emp_component_values = {}\n",
    "                    for comp_type, var_name in std_components.items():\n",
    "                        if var_name in empowered.columns:\n",
    "                            emp_component_values[comp_type] = empowered[var_name].mean()\n",
    "                    emp_status_values['empowered'] = emp_component_values\n",
    "                \n",
    "                # For disempowered households\n",
    "                if len(disempowered) > 0:\n",
    "                    disemp_component_values = {}\n",
    "                    for comp_type, var_name in std_components.items():\n",
    "                        if var_name in disempowered.columns:\n",
    "                            disemp_component_values[comp_type] = disempowered[var_name].mean()\n",
    "                    emp_status_values['disempowered'] = disemp_component_values\n",
    "                    \n",
    "                    # Calculate contribution to disempowerment\n",
    "                    contributions = {}\n",
    "                    weighted_deprivations = {}\n",
    "                    total_weighted_deprivation = 0\n",
    "                    \n",
    "                    for comp_type, var_name in std_components.items():\n",
    "                        if var_name in disempowered.columns:\n",
    "                            # Get component score for disempowered households\n",
    "                            score = disempowered[var_name].mean()\n",
    "                            \n",
    "                            # Calculate deprivation (1 - score)\n",
    "                            deprivation = 1 - score\n",
    "                            \n",
    "                            # Apply weight\n",
    "                            weight = get_component_weight(var_name)\n",
    "                            weighted_dep = deprivation * weight\n",
    "                            \n",
    "                            # Store weighted deprivation\n",
    "                            weighted_deprivations[comp_type] = weighted_dep\n",
    "                            total_weighted_deprivation += weighted_dep\n",
    "                    \n",
    "                    # Calculate percentage contribution\n",
    "                    if total_weighted_deprivation > 0:\n",
    "                        for comp_type, weighted_dep in weighted_deprivations.items():\n",
    "                            contributions[comp_type] = (weighted_dep / total_weighted_deprivation) * 100\n",
    "                        \n",
    "                        # Sort contributions by value\n",
    "                        sorted_contributions = sorted(contributions.items(), key=lambda x: x[1], reverse=True)\n",
    "                        print(f\"\\nContributions to disempowerment in Wave {wave}:\")\n",
    "                        for comp_type, contribution in sorted_contributions:\n",
    "                            print(f\"  {comp_type}: {contribution:.2f}%\")\n",
    "                        \n",
    "                        # Store contributions\n",
    "                        emp_status_values['contributions'] = contributions\n",
    "                \n",
    "                # Store by status values\n",
    "                component_analysis['by_status'][wave] = emp_status_values\n",
    "        \n",
    "        # Store component analysis\n",
    "        results['empowerment_components'][country_name] = component_analysis\n",
    "        \n",
    "        # PART 5: Changes Over Time\n",
    "        print(\"\\nAnalyzing changes in empowerment over time...\")\n",
    "        \n",
    "        if len(analysis_waves) >= 2:\n",
    "            # Calculate changes in empowerment rate\n",
    "            first_wave = min(analysis_waves)\n",
    "            last_wave = max(analysis_waves)\n",
    "            \n",
    "            # Get empowerment rate for first and last wave\n",
    "            if first_wave in wave_means and last_wave in wave_means:\n",
    "                first_rate = wave_means[first_wave]['mean']\n",
    "                last_rate = wave_means[last_wave]['mean']\n",
    "                \n",
    "                rate_change = last_rate - first_rate\n",
    "                rate_pct_change = (rate_change / first_rate) * 100 if first_rate > 0 else np.nan\n",
    "                \n",
    "                print(f\"Empowerment rate change from Wave {first_wave} to Wave {last_wave}:\")\n",
    "                print(f\"  {first_rate:.4f} -> {last_rate:.4f} (Change: {rate_change:.4f}, {rate_pct_change:.1f}%)\")\n",
    "                \n",
    "                # Store changes\n",
    "                results['empowerment_changes'][country_name] = {\n",
    "                    'first_wave': first_wave,\n",
    "                    'last_wave': last_wave,\n",
    "                    'emp_rate_first': first_rate,\n",
    "                    'emp_rate_last': last_rate,\n",
    "                    'emp_rate_change': rate_change,\n",
    "                    'emp_rate_pct_change': rate_pct_change\n",
    "                }\n",
    "                \n",
    "                # Calculate changes in component values\n",
    "                component_changes = {}\n",
    "                \n",
    "                if (first_wave in component_analysis['by_wave'] and \n",
    "                    last_wave in component_analysis['by_wave']):\n",
    "                    \n",
    "                    first_components = component_analysis['by_wave'][first_wave]\n",
    "                    last_components = component_analysis['by_wave'][last_wave]\n",
    "                    \n",
    "                    print(f\"Component changes from Wave {first_wave} to Wave {last_wave}:\")\n",
    "                    \n",
    "                    for comp_type in std_components.keys():\n",
    "                        if (comp_type in first_components and comp_type in last_components):\n",
    "                            first_value = first_components[comp_type]\n",
    "                            last_value = last_components[comp_type]\n",
    "                            \n",
    "                            change = last_value - first_value\n",
    "                            pct_change = (change / first_value) * 100 if first_value > 0 else np.nan\n",
    "                            \n",
    "                            print(f\"  {comp_type}: {first_value:.4f} -> {last_value:.4f} (Change: {change:.4f}, {pct_change:.1f}%)\")\n",
    "                            \n",
    "                            # Store component change\n",
    "                            component_changes[comp_type] = {\n",
    "                                'first': first_value,\n",
    "                                'last': last_value,\n",
    "                                'change': change,\n",
    "                                'pct_change': pct_change\n",
    "                            }\n",
    "                    \n",
    "                    # Add component changes to results\n",
    "                    results['empowerment_changes'][country_name]['components'] = component_changes\n",
    "        else:\n",
    "            print(\"Cannot analyze changes over time with less than 2 valid waves\")\n",
    "                \n",
    "    except Exception as e:\n",
    "        print(f\"Error processing {country_name}: {e}\")\n",
    "        traceback.print_exc()\n",
    "        debug_info[country_name] = {\"error\": str(e)}\n",
    "\n",
    "\n",
    "# Add this code after line ~1240 (after component_empowerment_df creation)\n",
    "\n",
    "# 9. Cash Crop Decision-Making Analysis\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating cash crop decision-making analysis...\")\n",
    "# Add this code after line ~1240 (after component_empowerment_df creation)\n",
    "\n",
    "# 9. Cash Crop Decision-Making Analysis\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating cash crop decision-making analysis (sellers only)...\")\n",
    "\n",
    "def create_cash_crop_decision_table(country_data, country_order):\n",
    "    \"\"\"\n",
    "    Creates an academic table showing women's decision-making over crop income\n",
    "    by household type (cash crops + staples vs staples only) across time\n",
    "    Analysis restricted to selling households (cci > 0)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Dictionary to store results for each country\n",
    "    cash_crop_results = {}\n",
    "    \n",
    "    for country in country_order:\n",
    "        if country not in country_data:\n",
    "            continue\n",
    "            \n",
    "        df = country_data[country]\n",
    "        print(f\"\\nAnalyzing cash crop decision-making for {country}\")\n",
    "        \n",
    "        # Check if required variables exist\n",
    "        required_vars = ['cashhouse', 'cashdec', 'sdec', 't', 'cci']\n",
    "        missing_vars = [var for var in required_vars if var not in df.columns]\n",
    "        \n",
    "        if missing_vars:\n",
    "            print(f\"  Missing variables for {country}: {missing_vars}\")\n",
    "            continue\n",
    "        \n",
    "        # Find waves with cashdec data AND sellers\n",
    "        waves_with_data = df[(df['cashdec'].notna()) & (df['cci'] > 0) & (df['cci'].notna())].groupby('t').size()\n",
    "        if len(waves_with_data) == 0:\n",
    "            print(f\"  No cashdec data available for sellers in {country}\")\n",
    "            continue\n",
    "            \n",
    "        available_waves = sorted(waves_with_data.index)\n",
    "        first_wave = available_waves[0]\n",
    "        last_wave = available_waves[-1]\n",
    "        \n",
    "        print(f\"  First wave with seller data: {first_wave}\")\n",
    "        print(f\"  Last wave with seller data: {last_wave}\")\n",
    "        \n",
    "        # Initialize results for this country\n",
    "        country_results = {\n",
    "            'first_wave': first_wave,\n",
    "            'last_wave': last_wave,\n",
    "            'data': {}\n",
    "        }\n",
    "        \n",
    "        # Analyze for each wave\n",
    "        for wave_label, wave in [('first', first_wave), ('last', last_wave)]:\n",
    "            wave_data = df[df['t'] == wave].copy()\n",
    "            \n",
    "            # Filter to only sellers (cci > 0)\n",
    "            total_before = len(wave_data)\n",
    "            wave_data = wave_data[(wave_data['cci'] > 0) & (wave_data['cci'].notna())]\n",
    "            total_after = len(wave_data)\n",
    "            \n",
    "            if total_before > 0:\n",
    "                print(f\"    Wave {wave}: {total_after} sellers out of {total_before} households ({total_after/total_before*100:.1f}%)\")\n",
    "            \n",
    "            # Get data for each household type\n",
    "            for hh_type in [0, 1]:\n",
    "                hh_data = wave_data[wave_data['cashhouse'] == hh_type]\n",
    "                \n",
    "                if len(hh_data) > 0:\n",
    "                    # Calculate what percentage of sellers are in this household type\n",
    "                    seller_percentage = (len(hh_data) / total_after * 100) if total_after > 0 else 0\n",
    "                    print(f\"      Cashhouse={hh_type}: {len(hh_data)} households ({seller_percentage:.1f}% of sellers)\")\n",
    "                    # Calculate statistics for cashdec\n",
    "                    cashdec_stats = {\n",
    "                        'n': hh_data['cashdec'].notna().sum(),\n",
    "                        'mean': hh_data['cashdec'].mean(),\n",
    "                        'std': hh_data['cashdec'].std(),\n",
    "                        'median': hh_data['cashdec'].median(),\n",
    "                        'min': hh_data['cashdec'].min(),\n",
    "                        'max': hh_data['cashdec'].max()\n",
    "                    }\n",
    "                    \n",
    "                    # Calculate statistics for sdec\n",
    "                    sdec_stats = {\n",
    "                        'n': hh_data['sdec'].notna().sum(),\n",
    "                        'mean': hh_data['sdec'].mean(),\n",
    "                        'std': hh_data['sdec'].std(),\n",
    "                        'median': hh_data['sdec'].median(),\n",
    "                        'min': hh_data['sdec'].min(),\n",
    "                        'max': hh_data['sdec'].max()\n",
    "                    }\n",
    "                    \n",
    "                    # Store results\n",
    "                    key = f\"{wave_label}_cashhouse_{hh_type}\"\n",
    "                    country_results['data'][key] = {\n",
    "                        'cashdec': cashdec_stats,\n",
    "                        'sdec': sdec_stats,\n",
    "                        'n_households': len(hh_data),\n",
    "                        'n_total_sellers': total_after  # Track total sellers in wave\n",
    "                    }\n",
    "                else:\n",
    "                    print(f\"    No seller data for cashhouse={hh_type} in wave {wave}\")\n",
    "        \n",
    "        # Calculate changes over time if both waves available\n",
    "        if first_wave != last_wave:\n",
    "            for hh_type in [0, 1]:\n",
    "                first_key = f\"first_cashhouse_{hh_type}\"\n",
    "                last_key = f\"last_cashhouse_{hh_type}\"\n",
    "                \n",
    "                if first_key in country_results['data'] and last_key in country_results['data']:\n",
    "                    # Calculate changes in means\n",
    "                    for var in ['cashdec', 'sdec']:\n",
    "                        first_mean = country_results['data'][first_key][var]['mean']\n",
    "                        last_mean = country_results['data'][last_key][var]['mean']\n",
    "                        \n",
    "                        if pd.notna(first_mean) and pd.notna(last_mean):\n",
    "                            change = last_mean - first_mean\n",
    "                            pct_change = (change / first_mean * 100) if first_mean != 0 else np.nan\n",
    "                            \n",
    "                            change_key = f\"change_cashhouse_{hh_type}\"\n",
    "                            if change_key not in country_results['data']:\n",
    "                                country_results['data'][change_key] = {}\n",
    "                            \n",
    "                            country_results['data'][change_key][var] = {\n",
    "                                'absolute': change,\n",
    "                                'percentage': pct_change\n",
    "                            }\n",
    "        \n",
    "        cash_crop_results[country] = country_results\n",
    "    \n",
    "    # Create formatted table for academic presentation\n",
    "    table_rows = []\n",
    "    \n",
    "    # Header rows\n",
    "    header1 = {'Variable': '', 'Period': ''}\n",
    "    header2 = {'Variable': 'Variable', 'Period': 'Period'}\n",
    "    \n",
    "    for country in country_order:\n",
    "        if country in cash_crop_results:\n",
    "            header1[f'{country}_Staples_Only'] = country\n",
    "            header1[f'{country}_Cash_and_Staples'] = ''\n",
    "            header2[f'{country}_Staples_Only'] = 'Staples Only'\n",
    "            header2[f'{country}_Cash_and_Staples'] = 'Cash & Staples'\n",
    "        else:\n",
    "            header1[f'{country}_Staples_Only'] = country\n",
    "            header1[f'{country}_Cash_and_Staples'] = ''\n",
    "            header2[f'{country}_Staples_Only'] = ''\n",
    "            header2[f'{country}_Cash_and_Staples'] = ''\n",
    "    \n",
    "    table_rows.append(header1)\n",
    "    table_rows.append(header2)\n",
    "    \n",
    "    # Add separator row\n",
    "    separator = {'Variable': '---', 'Period': '---'}\n",
    "    for country in country_order:\n",
    "        separator[f'{country}_Staples_Only'] = '---'\n",
    "        separator[f'{country}_Cash_and_Staples'] = '---'\n",
    "    table_rows.append(separator)\n",
    "    \n",
    "    # Data rows for each variable and time period\n",
    "    for var_code, var_name in [('cashdec', 'Women decide: cash crop income'), \n",
    "                                ('sdec', 'Women decide: staple income')]:\n",
    "        \n",
    "        # First wave\n",
    "        first_row = {'Variable': f'**{var_name}**', 'Period': 'First Wave'}\n",
    "        \n",
    "        for country in country_order:\n",
    "            if country in cash_crop_results:\n",
    "                results = cash_crop_results[country]\n",
    "                first_wave = results['first_wave']\n",
    "                \n",
    "                # Staples only (cashhouse=0)\n",
    "                key_0 = f\"first_cashhouse_0\"\n",
    "                if key_0 in results['data'] and var_code in results['data'][key_0]:\n",
    "                    stats = results['data'][key_0][var_code]\n",
    "                    n = stats['n']\n",
    "                    mean = stats['mean']\n",
    "                    std = stats['std']\n",
    "                    first_row[f'{country}_Staples_Only'] = f\"{mean:.3f} ({std:.3f}) [n={n}]\"\n",
    "                else:\n",
    "                    first_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                \n",
    "                # Cash and staples (cashhouse=1)\n",
    "                key_1 = f\"first_cashhouse_1\"\n",
    "                if key_1 in results['data'] and var_code in results['data'][key_1]:\n",
    "                    stats = results['data'][key_1][var_code]\n",
    "                    n = stats['n']\n",
    "                    mean = stats['mean']\n",
    "                    std = stats['std']\n",
    "                    first_row[f'{country}_Cash_and_Staples'] = f\"{mean:.3f} ({std:.3f}) [n={n}]\"\n",
    "                else:\n",
    "                    first_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "            else:\n",
    "                first_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                first_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "        \n",
    "        table_rows.append(first_row)\n",
    "        \n",
    "        # Last wave\n",
    "        last_row = {'Variable': '', 'Period': 'Last Wave'}\n",
    "        \n",
    "        for country in country_order:\n",
    "            if country in cash_crop_results:\n",
    "                results = cash_crop_results[country]\n",
    "                last_wave = results['last_wave']\n",
    "                \n",
    "                # Staples only (cashhouse=0)\n",
    "                key_0 = f\"last_cashhouse_0\"\n",
    "                if key_0 in results['data'] and var_code in results['data'][key_0]:\n",
    "                    stats = results['data'][key_0][var_code]\n",
    "                    n = stats['n']\n",
    "                    mean = stats['mean']\n",
    "                    std = stats['std']\n",
    "                    last_row[f'{country}_Staples_Only'] = f\"{mean:.3f} ({std:.3f}) [n={n}]\"\n",
    "                else:\n",
    "                    last_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                \n",
    "                # Cash and staples (cashhouse=1)\n",
    "                key_1 = f\"last_cashhouse_1\"\n",
    "                if key_1 in results['data'] and var_code in results['data'][key_1]:\n",
    "                    stats = results['data'][key_1][var_code]\n",
    "                    n = stats['n']\n",
    "                    mean = stats['mean']\n",
    "                    std = stats['std']\n",
    "                    last_row[f'{country}_Cash_and_Staples'] = f\"{mean:.3f} ({std:.3f}) [n={n}]\"\n",
    "                else:\n",
    "                    last_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "            else:\n",
    "                last_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                last_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "        \n",
    "        table_rows.append(last_row)\n",
    "        \n",
    "        # Change row (if applicable)\n",
    "        change_row = {'Variable': '', 'Period': 'Change (%)'}\n",
    "        has_changes = False\n",
    "        \n",
    "        for country in country_order:\n",
    "            if country in cash_crop_results:\n",
    "                results = cash_crop_results[country]\n",
    "                \n",
    "                # Staples only (cashhouse=0)\n",
    "                change_key_0 = f\"change_cashhouse_0\"\n",
    "                if (change_key_0 in results['data'] and \n",
    "                    var_code in results['data'][change_key_0]):\n",
    "                    pct_change = results['data'][change_key_0][var_code]['percentage']\n",
    "                    if pd.notna(pct_change):\n",
    "                        change_row[f'{country}_Staples_Only'] = f\"{pct_change:+.1f}%\"\n",
    "                        has_changes = True\n",
    "                    else:\n",
    "                        change_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                else:\n",
    "                    change_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                \n",
    "                # Cash and staples (cashhouse=1)\n",
    "                change_key_1 = f\"change_cashhouse_1\"\n",
    "                if (change_key_1 in results['data'] and \n",
    "                    var_code in results['data'][change_key_1]):\n",
    "                    pct_change = results['data'][change_key_1][var_code]['percentage']\n",
    "                    if pd.notna(pct_change):\n",
    "                        change_row[f'{country}_Cash_and_Staples'] = f\"{pct_change:+.1f}%\"\n",
    "                        has_changes = True\n",
    "                    else:\n",
    "                        change_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "                else:\n",
    "                    change_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "            else:\n",
    "                change_row[f'{country}_Staples_Only'] = \"---\"\n",
    "                change_row[f'{country}_Cash_and_Staples'] = \"---\"\n",
    "        \n",
    "        if has_changes:\n",
    "            table_rows.append(change_row)\n",
    "        \n",
    "        # Add blank row between variables\n",
    "        if var_code == 'cashdec':\n",
    "            blank_row = {col: '' for col in table_rows[0].keys()}\n",
    "            table_rows.append(blank_row)\n",
    "    \n",
    "    # Add notes\n",
    "    note1 = {'Variable': 'Notes:', 'Period': ''}\n",
    "    note2 = {'Variable': 'Values show mean (std. dev.) [n]', 'Period': ''}\n",
    "    note3 = {'Variable': 'Analysis restricted to selling households (cci > 0)', 'Period': ''}\n",
    "    note4 = {'Variable': 'First/Last waves vary by country based on data availability', 'Period': ''}\n",
    "    \n",
    "    for country in country_order:\n",
    "        note1[f'{country}_Staples_Only'] = ''\n",
    "        note1[f'{country}_Cash_and_Staples'] = ''\n",
    "        note2[f'{country}_Staples_Only'] = ''\n",
    "        note2[f'{country}_Cash_and_Staples'] = ''\n",
    "        note3[f'{country}_Staples_Only'] = ''\n",
    "        note3[f'{country}_Cash_and_Staples'] = ''\n",
    "        note4[f'{country}_Staples_Only'] = ''\n",
    "        note4[f'{country}_Cash_and_Staples'] = ''\n",
    "    \n",
    "    table_rows.extend([note1, note2, note3, note4])\n",
    "    \n",
    "    # Create DataFrame\n",
    "    cash_crop_df = pd.DataFrame(table_rows)\n",
    "    \n",
    "    # Also create a summary of waves used\n",
    "    wave_summary_rows = []\n",
    "    for country in country_order:\n",
    "        if country in cash_crop_results:\n",
    "            results = cash_crop_results[country]\n",
    "            \n",
    "            # Get seller counts from stored data\n",
    "            first_sellers_0 = results['data'].get('first_cashhouse_0', {}).get('n_households', 0)\n",
    "            first_sellers_1 = results['data'].get('first_cashhouse_1', {}).get('n_households', 0)\n",
    "            last_sellers_0 = results['data'].get('last_cashhouse_0', {}).get('n_households', 0)\n",
    "            last_sellers_1 = results['data'].get('last_cashhouse_1', {}).get('n_households', 0)\n",
    "            \n",
    "            wave_summary_rows.append({\n",
    "                'Country': country,\n",
    "                'First Wave': results['first_wave'],\n",
    "                'Last Wave': results['last_wave'],\n",
    "                'Years Spanned': results['last_wave'] - results['first_wave'] if results['last_wave'] != results['first_wave'] else 0,\n",
    "                'First Wave Sellers (Staples Only)': first_sellers_0,\n",
    "                'First Wave Sellers (Cash & Staples)': first_sellers_1,\n",
    "                'Last Wave Sellers (Staples Only)': last_sellers_0,\n",
    "                'Last Wave Sellers (Cash & Staples)': last_sellers_1\n",
    "            })\n",
    "    \n",
    "    wave_summary_df = pd.DataFrame(wave_summary_rows)\n",
    "    \n",
    "    return cash_crop_df, wave_summary_df, cash_crop_results\n",
    "\n",
    "# Create the cash crop decision table\n",
    "cash_crop_table, wave_summary_table, cash_crop_raw_results = create_cash_crop_decision_table(\n",
    "    results['country_data'], \n",
    "    country_order\n",
    ")\n",
    "\n",
    "# Store in results for later use\n",
    "results['cash_crop_analysis'] = {\n",
    "    'table': cash_crop_table,\n",
    "    'wave_summary': wave_summary_table,\n",
    "    'raw_results': cash_crop_raw_results\n",
    "}\n",
    "\n",
    "# Create output tables\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating output tables...\")\n",
    "\n",
    "# 1. Empowerment Classification Table\n",
    "classification_rows = []\n",
    "for country in country_order:\n",
    "    if country in results['empowerment_classification']:\n",
    "        data = results['empowerment_classification'][country]\n",
    "        row = {\n",
    "            'Country': country,\n",
    "            'Total Households': data.get('Total Households', 0),\n",
    "            'Always Empowered (%)': data.get('Always Empowered (%)', np.nan),\n",
    "            'Never Empowered (%)': data.get('Never Empowered (%)', np.nan),\n",
    "            'Mixed (%)': data.get('Mixed (%)', np.nan),\n",
    "            'Became Empowered (%)': data.get('Became Empowered (%)', np.nan),\n",
    "            'Became Disempowered (%)': data.get('Became Disempowered (%)', np.nan),\n",
    "            'Fluctuating (%)': data.get('Fluctuating (%)', np.nan)\n",
    "        }\n",
    "        classification_rows.append(row)\n",
    "\n",
    "classification_df = pd.DataFrame(classification_rows)\n",
    "\n",
    "# 2. Empowerment Means Table\n",
    "means_rows = []\n",
    "all_waves = set()\n",
    "for country, wave_data in results['empowerment_means'].items():\n",
    "    all_waves.update(wave_data.keys())\n",
    "all_waves = sorted(all_waves)\n",
    "\n",
    "for country in country_order:\n",
    "    if country in results['empowerment_means']:\n",
    "        row = {'Country': country}\n",
    "        wave_data = results['empowerment_means'][country]\n",
    "        \n",
    "        for wave in all_waves:\n",
    "            if wave in wave_data:\n",
    "                row[f'Wave {wave}'] = wave_data[wave]['mean']\n",
    "                row[f'Std Dev Wave {wave}'] = wave_data[wave]['std']\n",
    "            else:\n",
    "                row[f'Wave {wave}'] = np.nan\n",
    "                row[f'Std Dev Wave {wave}'] = np.nan\n",
    "        \n",
    "        means_rows.append(row)\n",
    "\n",
    "means_df = pd.DataFrame(means_rows)\n",
    "\n",
    "# 3. Standardized Component Table\n",
    "component_rows = []\n",
    "all_component_types = ['prod', 'inc', 'asset', 'cred', 'time']\n",
    "\n",
    "for country in country_order:\n",
    "    if country in results['standardized_components']:\n",
    "        row = {'Country': country}\n",
    "        std_components = results['standardized_components'][country]\n",
    "        \n",
    "        # Most recent wave's data\n",
    "        last_wave = None\n",
    "        last_wave_data = None\n",
    "        \n",
    "        if country in results['empowerment_components']:\n",
    "            component_data = results['empowerment_components'][country]\n",
    "            if 'by_wave' in component_data:\n",
    "                waves = sorted(component_data['by_wave'].keys())\n",
    "                if waves:\n",
    "                    last_wave = waves[-1]\n",
    "                    last_wave_data = component_data['by_wave'][last_wave]\n",
    "        \n",
    "        # Add component values\n",
    "        for comp_type in all_component_types:\n",
    "            if (comp_type in std_components and \n",
    "                last_wave_data and \n",
    "                comp_type in last_wave_data):\n",
    "                row[comp_type] = last_wave_data[comp_type]\n",
    "            else:\n",
    "                row[comp_type] = np.nan\n",
    "        \n",
    "        component_rows.append(row)\n",
    "\n",
    "component_df = pd.DataFrame(component_rows)\n",
    "\n",
    "# 4. Academic Format T-Test Tables\n",
    "print(\"\\nCreating academic format t-test tables...\")\n",
    "\n",
    "# Define variable categories\n",
    "variable_categories = {\n",
    "    \"Household characteristics\": [\n",
    "        ('cci', 'Commercialization index'),\n",
    "        ('fpsh', 'Share of food consumed purchased'),\n",
    "        ('NonfIncShare', 'Share of off-farm income'),\n",
    "        ('poor2', 'Below poverty line'),\n",
    "        ('quint', 'Consumption quintile'),\n",
    "        ('head_age', 'Head of household\\'s age'),\n",
    "        ('head_sex', 'Head is male'),\n",
    "        ('ac_workforce', 'Avg. Years of educ. of HH workforce'),\n",
    "        ('dependency_ratio', 'Dependency ratio'),\n",
    "        ('hhsize', 'Household size')\n",
    "    ],\n",
    "    \"Meso variables\": [\n",
    "        ('road_dense', 'Road Density')\n",
    "    ],\n",
    "    \"Farm characteristics\": [\n",
    "        ('farmsize', 'Cultivated area (ha)')\n",
    "    ]\n",
    "}\n",
    "\n",
    "# Replace the academic t-test tables section\n",
    "print(\"\\nCreating academic format t-test tables...\")\n",
    "\n",
    "# Variables to compare with t-tests\n",
    "t_test_vars = [\n",
    "    'cci', 'fpsh', 'NonfIncShare', 'road_dense', 'poor2', 'quint', \n",
    "    'head_age', 'head_sex', 'ac_workforce', 'dependency_ratio', \n",
    "    'hhsize', 'farmsize', 'overall_shock'\n",
    "]\n",
    "\n",
    "# Define country groups with descriptive names\n",
    "country_groups = {\n",
    "    'Ethiopia_Malawi': ['Ethiopia', 'Malawi'],\n",
    "    'Tanzania_Uganda': ['Tanzania', 'Uganda'],\n",
    "    'Nigeria': ['Nigeria']\n",
    "}\n",
    "\n",
    "# Create academic format t-test tables using the new function\n",
    "academic_ttest_tables = create_academic_ttest_tables(results['country_data'], t_test_vars, country_groups, variable_categories)\n",
    "\n",
    "# 5. Alternative Shapley Decomposition Analysis based on component contributions\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Extracting Shapley decomposition from component contributions...\")\n",
    "shapley_results = extract_shapley_from_components(results, country_order)\n",
    "\n",
    "# Create Shapley charts\n",
    "print(\"\\nCreating Shapley decomposition visualizations...\")\n",
    "create_shapley_charts(shapley_results, country_order, fig_dir)\n",
    "\n",
    "# 6. Component trends over time\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Analyzing component trends over time...\")\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
    "component_trend_tables = analyze_component_trends(results, country_order, fig_dir, colors)\n",
    "\n",
    "# 7. Component t-test tables\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating component t-test tables...\")\n",
    "component_ttest_tables = create_component_ttest_tables(results, country_groups)\n",
    "\n",
    "# 8. Component empowerment percentages\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating component empowerment percentage table...\")\n",
    "component_empowerment_df = create_component_empowerment_table(results, country_order)\n",
    "\n",
    "# Create Visualizations\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Creating visualizations...\")\n",
    "\n",
    "# 1. Empowerment trend plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']\n",
    "\n",
    "for i, country in enumerate(country_order):\n",
    "    if country in results['empowerment_means']:\n",
    "        data = results['empowerment_means'][country]\n",
    "        waves = sorted(data.keys())\n",
    "        rates = [data[wave]['mean'] for wave in waves]\n",
    "        \n",
    "        plt.plot(waves, rates, marker='o', linewidth=2, label=country, color=colors[i % len(colors)])\n",
    "        \n",
    "        # Add data labels\n",
    "        for j, (wave, rate) in enumerate(zip(waves, rates)):\n",
    "            plt.text(wave, rate + 0.02, f'{rate:.2f}', ha='center', va='bottom', fontsize=9, color=colors[i % len(colors)])\n",
    "\n",
    "plt.xlabel('Wave')\n",
    "plt.ylabel('Empowerment Rate')\n",
    "plt.title('A-WEAI Empowerment Rates Across Waves by Country')\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.legend()\n",
    "plt.ylim(0, 1.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(fig_dir, 'empowerment_trend.png'), dpi=300)\n",
    "plt.savefig(os.path.join(fig_dir, 'empowerment_trend.pdf'), format='pdf')\n",
    "plt.close()\n",
    "\n",
    "# 2. Empowerment classification stacked bar chart\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "countries = []\n",
    "always = []\n",
    "never = []\n",
    "became = []\n",
    "disempowered = []\n",
    "fluctuating = []\n",
    "\n",
    "for country in country_order:\n",
    "    if country in results['empowerment_classification']:\n",
    "        data = results['empowerment_classification'][country]\n",
    "        \n",
    "        countries.append(country)\n",
    "        always.append(data.get('Always Empowered (%)', 0))\n",
    "        never.append(data.get('Never Empowered (%)', 0))\n",
    "        \n",
    "        # Convert subcategories to direct percentages\n",
    "        mixed_pct = data.get('Mixed (%)', 0)\n",
    "        became_pct = data.get('Became Empowered (%)', 0)\n",
    "        disempowered_pct = data.get('Became Disempowered (%)', 0)\n",
    "        fluct_pct = data.get('Fluctuating (%)', 0)\n",
    "        \n",
    "        # Calculate percentages of total\n",
    "        became.append(became_pct * mixed_pct / 100 if pd.notnull(became_pct) and pd.notnull(mixed_pct) else 0)\n",
    "        disempowered.append(disempowered_pct * mixed_pct / 100 if pd.notnull(disempowered_pct) and pd.notnull(mixed_pct) else 0)\n",
    "        fluctuating.append(fluct_pct * mixed_pct / 100 if pd.notnull(fluct_pct) and pd.notnull(mixed_pct) else 0)\n",
    "\n",
    "# Create stacked bar chart\n",
    "bar_width = 0.7\n",
    "x_positions = np.arange(len(countries))\n",
    "\n",
    "# Define colors\n",
    "colors = {\n",
    "    'Always Empowered': '#2ecc71',\n",
    "    'Became Empowered': '#3498db',\n",
    "    'Fluctuating': '#f39c12',\n",
    "    'Became Disempowered': '#e74c3c',\n",
    "    'Never Empowered': '#95a5a6'\n",
    "}\n",
    "\n",
    "# Create bars with baseline at zero\n",
    "bottoms = np.zeros(len(countries))\n",
    "\n",
    "# Create bars\n",
    "plt.bar(x_positions, always, bar_width, label='Always Empowered', color=colors['Always Empowered'], bottom=bottoms)\n",
    "bottoms += np.array(always)\n",
    "\n",
    "plt.bar(x_positions, became, bar_width, label='Became Empowered', color=colors['Became Empowered'], bottom=bottoms)\n",
    "bottoms += np.array(became)\n",
    "\n",
    "plt.bar(x_positions, fluctuating, bar_width, label='Fluctuating', color=colors['Fluctuating'], bottom=bottoms)\n",
    "bottoms += np.array(fluctuating)\n",
    "\n",
    "plt.bar(x_positions, disempowered, bar_width, label='Became Disempowered', color=colors['Became Disempowered'], bottom=bottoms)\n",
    "bottoms += np.array(disempowered)\n",
    "\n",
    "plt.bar(x_positions, never, bar_width, label='Never Empowered', color=colors['Never Empowered'], bottom=bottoms)\n",
    "\n",
    "# Add value labels\n",
    "def add_value_labels(values, bottoms, threshold=5):\n",
    "    for i, (v, b) in enumerate(zip(values, bottoms)):\n",
    "        if v >= threshold:  # Only add labels for segments >= threshold\n",
    "            plt.text(i, b + v/2, f'{v:.1f}%', ha='center', va='center', color='white', fontweight='bold')\n",
    "\n",
    "# Reset bottoms for label positioning\n",
    "bottoms = np.zeros(len(countries))\n",
    "add_value_labels(always, bottoms)\n",
    "bottoms += np.array(always)\n",
    "add_value_labels(became, bottoms)\n",
    "bottoms += np.array(became)\n",
    "add_value_labels(fluctuating, bottoms)\n",
    "bottoms += np.array(fluctuating)\n",
    "add_value_labels(disempowered, bottoms)\n",
    "bottoms += np.array(disempowered)\n",
    "add_value_labels(never, bottoms)\n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Percentage of Households')\n",
    "plt.title('Empowerment Classification by Country')\n",
    "plt.xticks(x_positions, countries)\n",
    "plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=5)\n",
    "plt.ylim(0, 100)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(fig_dir, 'empowerment_classification.png'), dpi=300)\n",
    "plt.savefig(os.path.join(fig_dir, 'empowerment_classification.pdf'), format='pdf')\n",
    "plt.close()\n",
    "\n",
    "# 3. Component scores by country\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Create data for the plot\n",
    "component_data = {comp_type: [] for comp_type in all_component_types}\n",
    "countries_with_data = []\n",
    "\n",
    "for country in country_order:\n",
    "    if country in component_df.index:\n",
    "        countries_with_data.append(country)\n",
    "        for comp_type in all_component_types:\n",
    "            if comp_type in component_df.columns:\n",
    "                component_data[comp_type].append(component_df.loc[country, comp_type])\n",
    "            else:\n",
    "                component_data[comp_type].append(0)\n",
    "\n",
    "# Set up the plot\n",
    "x_positions = np.arange(len(countries_with_data))\n",
    "bar_width = 0.15\n",
    "offsets = np.linspace(-(bar_width * 2), (bar_width * 2), len(all_component_types))\n",
    "\n",
    "# Set colors for components\n",
    "component_colors = {\n",
    "    'prod': '#1f77b4',  # Blue\n",
    "    'inc': '#ff7f0e',   # Orange\n",
    "    'asset': '#2ca02c', # Green\n",
    "    'cred': '#d62728',  # Red\n",
    "    'time': '#9467bd'   # Purple\n",
    "}\n",
    "\n",
    "# Create grouped bars\n",
    "for i, comp_type in enumerate(all_component_types):\n",
    "    if countries_with_data and comp_type in component_data:\n",
    "        values = component_data[comp_type]\n",
    "        plt.bar(x_positions + offsets[i], values, bar_width, \n",
    "                label=comp_type.capitalize(), color=component_colors[comp_type])\n",
    "\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Component Score')\n",
    "plt.title('A-WEAI Component Scores by Country')\n",
    "plt.xticks(x_positions, countries_with_data)\n",
    "plt.legend()\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(fig_dir, 'component_scores.png'), dpi=300)\n",
    "plt.savefig(os.path.join(fig_dir, 'component_scores.pdf'), format='pdf')\n",
    "plt.close()\n",
    "\n",
    "# Write results to Excel\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Writing results to Excel...\")\n",
    "\n",
    "try:\n",
    "    # First try to open the original path\n",
    "    excel_path = output_path\n",
    "    try_count = 0\n",
    "    max_tries = 3\n",
    "    \n",
    "    while try_count < max_tries:\n",
    "        try:\n",
    "            with pd.ExcelWriter(excel_path, engine='openpyxl') as writer:\n",
    "                # Main tables\n",
    "                classification_df.to_excel(writer, sheet_name='Empowerment_Classification', index=False)\n",
    "                print(\"- Wrote Empowerment Classification sheet\")\n",
    "                \n",
    "                means_df.to_excel(writer, sheet_name='Empowerment_Means', index=False)\n",
    "                print(\"- Wrote Empowerment Means sheet\")\n",
    "                \n",
    "                component_df.to_excel(writer, sheet_name='Component_Summary', index=False)\n",
    "                print(\"- Wrote Component Summary sheet\")\n",
    "                \n",
    "                # T-test tables\n",
    "                for group_name, table in academic_ttest_tables.items():\n",
    "                    if not table.empty:\n",
    "                        # Make sure sheet name is valid\n",
    "                        safe_name = f'T_Test_{group_name}'\n",
    "                        if len(safe_name) > 31:  # Excel sheet name length limit\n",
    "                            safe_name = safe_name[:31]\n",
    "                        table.to_excel(writer, sheet_name=safe_name, index=False)\n",
    "                        print(f\"- Wrote T-Test {group_name} sheet\")\n",
    "                \n",
    "                # Component t-test tables\n",
    "                for group_name, table in component_ttest_tables.items():\n",
    "                    if not table.empty:\n",
    "                        sheet_name = f'Component_TTest_{group_name}'\n",
    "                        if len(sheet_name) > 31:  # Excel sheet name length limit\n",
    "                            sheet_name = sheet_name[:31]\n",
    "                        table.to_excel(writer, sheet_name=sheet_name, index=False)\n",
    "                        print(f\"- Wrote Component T-Test {group_name} sheet\")\n",
    "                \n",
    "                # Component empowerment percentage table\n",
    "                if not component_empowerment_df.empty:\n",
    "                    component_empowerment_df.to_excel(writer, sheet_name='Component_Empowerment_Pct', index=False)\n",
    "                    print(\"- Wrote Component Empowerment Percentages sheet\")\n",
    "                \n",
    "                # Write component trend tables\n",
    "                for country, trend_df in component_trend_tables.items():\n",
    "                    sheet_name = f'Component_Trends_{country}'\n",
    "                    if len(sheet_name) > 31:  # Excel sheet name length limit\n",
    "                        sheet_name = sheet_name[:31]\n",
    "                    trend_df.to_excel(writer, sheet_name=sheet_name)\n",
    "                    print(f\"- Wrote Component Trends {country} sheet\")\n",
    "                \n",
    "                # Summary sheet\n",
    "                summary_df = pd.DataFrame({\n",
    "                    'Country': country_order,\n",
    "                    'Data Available': [country in results['empowerment_classification'] for country in country_order],\n",
    "                    'Error': [debug_info.get(country, {}).get('error', '') for country in country_order]\n",
    "                })\n",
    "                summary_df.to_excel(writer, sheet_name='Summary', index=False)\n",
    "                print(\"- Wrote Summary sheet\")\n",
    "                \n",
    "                # Write Shapley decomposition results\n",
    "                shapley_df = pd.DataFrame(index=all_component_types)\n",
    "                \n",
    "                for country in country_order:\n",
    "                    if country in shapley_results:\n",
    "                        values = shapley_results[country]\n",
    "                        # Convert to percentages\n",
    "                        shapley_df[country] = [values.get(comp, 0) * 100 for comp in all_component_types]\n",
    "                \n",
    "                # Add row with totals\n",
    "                shapley_df.loc['Total'] = shapley_df.sum()\n",
    "                \n",
    "                # Format as percentages\n",
    "                shapley_df = shapley_df.round(1)\n",
    "                \n",
    "                # Write to Excel\n",
    "                shapley_df.to_excel(writer, sheet_name='Shapley_Decomposition')\n",
    "                print(\"- Wrote Shapley Decomposition sheet\")\n",
    "\n",
    "                # Add this code within the Excel writer block, after the other sheets are written\n",
    "\n",
    "                # Cash crop decision-making table\n",
    "                if 'cash_crop_analysis' in results and results['cash_crop_analysis']['table'] is not None:\n",
    "                    cash_crop_table = results['cash_crop_analysis']['table']\n",
    "                    if not cash_crop_table.empty:\n",
    "                        cash_crop_table.to_excel(writer, sheet_name='Cash_Crop_Decisions', index=False)\n",
    "                        print(\"- Wrote Cash Crop Decisions sheet\")\n",
    "                    \n",
    "                    # Wave summary for cash crop analysis\n",
    "                    wave_summary = results['cash_crop_analysis']['wave_summary']\n",
    "                    if not wave_summary.empty:\n",
    "                        wave_summary.to_excel(writer, sheet_name='Cash_Crop_Wave_Summary', index=False)\n",
    "                        print(\"- Wrote Cash Crop Wave Summary sheet\")\n",
    "            \n",
    "            print(f\"\\nAnalysis completed successfully. Results saved to {excel_path}\")\n",
    "            print(f\"Visualizations saved to {fig_dir}\")\n",
    "            break  # If we get here, writing was successful\n",
    "            \n",
    "        except PermissionError:\n",
    "            try_count += 1\n",
    "            if try_count >= max_tries:\n",
    "                # Try an alternative path in desktop folder\n",
    "                print(f\"Unable to write to {excel_path} after {max_tries} attempts. File may be open or protected.\")\n",
    "                \n",
    "                # Generate alternative path in the same directory\n",
    "                base_path = os.path.dirname(excel_path)\n",
    "                file_name = os.path.basename(excel_path)\n",
    "                alt_file_name = f\"A-WEAI_Analysis_Results_new_{try_count}.xlsx\"\n",
    "                excel_path = os.path.join(base_path, alt_file_name)\n",
    "                \n",
    "                print(f\"Trying alternative file path: {excel_path}\")\n",
    "            else:\n",
    "                print(f\"Permission error on attempt {try_count}. File may be open. Retrying...\")\n",
    "                time.sleep(2)  # Wait a bit before retrying\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Error writing to Excel: {e}\")\n",
    "    traceback.print_exc()\n",
    "    print(\"\\nOutput files may not have been saved completely. Please check the specified directories.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "17c4b65a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "countries = [\"ETH\", \"MLW\", \"TZN\", \"UGD\", \"NGR\"]\n",
    "\n",
    "# --- Main effects: coef on cci ---\n",
    "main_effect = {\n",
    "    \"Productive decisions\":  [0.8592243, -0.0021469, -0.0176297, -0.0102826, -0.104566],  # NGR missing in logs\n",
    "    \"Income decisions\":      [0.4458608,  0.4626989,  0.4509938, -0.0064591, 0.1772762],\n",
    "    \"Asset ownership\":       [0.0709995, -0.0405044, -0.0344130,  0.0108184, 0.0579905],\n",
    "    \"Control credit\":        [-0.1009771, 0.0415698, -0.0413478,           np.nan, 0.0045244],\n",
    "    \"Time allocation\":       [-0.0577067, 0.0009041, -0.0506516, -0.0393570, -0.0135406],\n",
    "}\n",
    "se_main = {\n",
    "    \"Productive decisions\":  [0.0846972, 0.0439654, 0.0284626, 0.0241116, 0.0336188],\n",
    "    \"Income decisions\":      [0.0695372, 0.0563867, 0.0332230, 0.0370415, 0.0392156],\n",
    "    \"Asset ownership\":       [0.0511414, 0.0525484, 0.0275916, 0.0294342, 0.0332760],\n",
    "    \"Control credit\":        [0.0652436, 0.0401509, 0.0227333,           np.nan, 0.0486569],\n",
    "    \"Time allocation\":       [0.0479766, 0.0246145, 0.0273117, 0.0325934, 0.0313840],\n",
    "}\n",
    "\n",
    "# --- Interactions: coef on cci × cash_sale ---\n",
    "interaction = {\n",
    "    \"Productive decisions\":  [-0.5868668, -0.0738484, -0.0797698, -0.0048113, 0.1215],\n",
    "    \"Income decisions\":      [-0.1929978, -0.0584103, -0.1928864,  0.0343297, 0.0013636],\n",
    "    \"Asset ownership\":       [-0.0207748,  0.0243667, -0.0000613, -0.0052569, -0.1271666],\n",
    "    \"Control credit\":        [ 0.1331276, -0.0802290,  0.0666862,           np.nan,  0.0637887],\n",
    "    \"Time allocation\":       [ 0.0524542,  0.0368312,  0.0075727,  0.0306130,  0.0794940],\n",
    "}\n",
    "se_int = {\n",
    "    \"Productive decisions\":  [0.0925540, 0.0589742, 0.0369124, 0.0280739, 0.05599],\n",
    "    \"Income decisions\":      [0.0776315, 0.0728343, 0.0419222, 0.0413286, 0.0663401],\n",
    "    \"Asset ownership\":       [0.0583818, 0.0680594, 0.0345909, 0.0351667, 0.0597891],\n",
    "    \"Control credit\":        [0.0724723, 0.0519646, 0.0283495,           np.nan, 0.0868536],\n",
    "    \"Time allocation\":       [0.0527554, 0.0368074, 0.0348377, 0.0392379, 0.0501640],\n",
    "}\n",
    "\n",
    "def plot_grid(coeff_dict, se_dict, title_prefix):\n",
    "    fig, axes = plt.subplots(2, 3, figsize=(11, 6))\n",
    "    axes = axes.flatten()\n",
    "    for i, (outcome, coefs) in enumerate(coeff_dict.items()):\n",
    "        ax = axes[i]\n",
    "        ses = se_dict[outcome]\n",
    "        y = np.arange(len(countries))\n",
    "        valid = ~np.isnan(coefs)\n",
    "        ax.errorbar(np.array(coefs)[valid],\n",
    "                    y[valid],\n",
    "                    xerr=1.96*np.array(ses)[valid],\n",
    "                    fmt='o',\n",
    "                    capsize=3)\n",
    "        ax.axvline(0, linestyle='--', linewidth=1)\n",
    "        ax.set_yticks(y[valid])\n",
    "        ax.set_yticklabels(np.array(countries)[valid])\n",
    "        ax.set_xlabel(\"Coefficient\")\n",
    "        ax.set_title(outcome, fontsize=10)\n",
    "        ax.invert_yaxis()\n",
    "    # turn off any unused subplot\n",
    "    for j in range(len(coeff_dict), len(axes)):\n",
    "        axes[j].axis('off')\n",
    "    fig.suptitle(f\"{title_prefix}: association by country\", fontsize=12)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# ----------------------------\n",
    "# 2) Plots\n",
    "# ----------------------------\n",
    "plot_grid(main_effect, se_main, \"Commercialization index\")\n",
    "plot_grid(interaction, se_int, \"Interaction: Commercialization x Share of non-staples in sales\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "869af23b",
   "metadata": {},
   "source": [
    "PSM-DID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bd78ef1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "============================================================\n",
      "********** NOW RUNNING: Malawi **********\n",
      "============================================================\n",
      "\n",
      "Malawi data loaded:\n",
      "  Observations: 7187\n",
      "  Time periods: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Number of households: 2814\n",
      "  'cc' variable found with 6993 non-missing values\n",
      "  Note: Malawi is NOT restricted to start from the second wave.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 1: Treatment defined by 'cci' *****\n",
      "***** (All households: Never sellers vs Late sellers) *****\n",
      "**************************************************\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 1304\n",
      "\n",
      "  Treatment assignment for cci:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 1304\n",
      "  Control group: 286 households\n",
      "  Treatment group: 350 households\n",
      "  Excluded: 668 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 636\n",
      "  Total observations at baseline: 636\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 286 households\n",
      "    Treatment (1): 350 households\n",
      "  Converting categorical variable 'head_sex' to numeric\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 286\n",
      "  Treatment households: 350\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 507/636 (79.7%)\n",
      "  Mean SMD: 0.068\n",
      "  Max SMD: 0.161\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 601/636 (94.5%)\n",
      "  Mean SMD: 0.090\n",
      "  Max SMD: 0.209\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 619/636 (97.3%)\n",
      "  Mean SMD: 0.109\n",
      "  Max SMD: 0.252\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 519/636 (81.6%)\n",
      "  Mean SMD: 0.091\n",
      "  Max SMD: 0.275\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 598/636 (94.0%)\n",
      "  Mean SMD: 0.106\n",
      "  Max SMD: 0.255\n",
      "\n",
      "kernel:\n",
      "  Matched: 636/636 (100.0%)\n",
      "  Mean SMD: 0.147\n",
      "  Max SMD: 0.443\n",
      "\n",
      "radius:\n",
      "  Matched: 628/636 (98.7%)\n",
      "  Mean SMD: 0.133\n",
      "  Max SMD: 0.409\n",
      "\n",
      "stratification:\n",
      "  Matched: 636/636 (100.0%)\n",
      "  Mean SMD: 0.147\n",
      "  Max SMD: 0.443\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Malawi: DID Results (from 'cci' analysis) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Malawi - CCI (never sellers vs late sellers)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               493    0   493\n",
      "1.0               529  822  1351\n",
      "All              1022  822  1844\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.247193, unique_values=2\n",
      "  treatment_group: variance=0.195982, unique_values=2\n",
      "  post: variance=0.247193, unique_values=2\n",
      "  emp: variance=0.249985, unique_values=2\n",
      "  lognf: variance=17.300571, unique_values=1045\n",
      "  educh: variance=0.177746, unique_values=2\n",
      "  head_age: variance=264.912574, unique_values=82\n",
      "  head_sex: categorical variable, unique_values=2\n",
      "  dependency_ratio: variance=1.077249, unique_values=42\n",
      "  vharvest: variance=1507583197184.000000, unique_values=1640\n",
      "  farmsize: variance=0.502881, unique_values=553\n",
      "  asset: variance=3.734740, unique_values=298\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1844\n",
      "  Unique households: 507\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 493 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 529 obs\n",
      "     Treatment=1, Post=1: 822 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 1821/1844 (98.8%)\n",
      "  Mean: 0.512, Std: 0.500\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + lognf + educh + head_age + head_sex + dependency_ratio + vharvest + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0639\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.3966\n",
      "No. Observations:                1778   R-squared (Within):               0.1751\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.0364\n",
      "Time:                        15:47:30   Log-likelihood                   -706.27\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      9.5934\n",
      "Entities:                         502   P-value                           0.0000\n",
      "Avg Obs:                       3.5418   Distribution:                  F(9,1264)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             95.663\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       4   Distribution:                  F(9,1264)\n",
      "Avg Obs:                       444.50                                           \n",
      "Min Obs:                       415.00                                           \n",
      "Max Obs:                       482.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.2632     0.0459     5.7325     0.0000      0.1731      0.3532\n",
      "treatment_group      0.5376     0.1379     3.8998     0.0001      0.2672      0.8081\n",
      "lognf                0.0115     0.0038     2.9978     0.0028      0.0040      0.0190\n",
      "educh               -0.0402     0.0456    -0.8812     0.3784     -0.1297      0.0493\n",
      "head_age            -0.0036     0.0016    -2.2561     0.0242     -0.0067     -0.0005\n",
      "head_sex[Female]     0.1418     0.0502     2.8230     0.0048      0.0433      0.2403\n",
      "dependency_ratio     0.0039     0.0201     0.1938     0.8464     -0.0355      0.0432\n",
      "vharvest         -2.296e-08  1.503e-08    -1.5273     0.1269  -5.245e-08   6.531e-09\n",
      "farmsize             0.0615     0.0274     2.2466     0.0248      0.0078      0.1152\n",
      "asset                0.0127     0.0165     0.7695     0.4418     -0.0197      0.0450\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.4126\n",
      "P-value: 0.0000\n",
      "Distribution: F(504,1264)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Malawi - cci ---\n",
      "Event times for treated units: [-8.0, -6.0, -5.0, -3.0, -2.0, 0.0, 3.0, 6.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m8.0 + treat_event_m6.0 + treat_event_m5.0 + treat_event_m3.0 + treat_event_0.0 + treat_event_3.0 + treat_event_6.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0412\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.2245\n",
      "No. Observations:                1821   R-squared (Within):               0.1237\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.1977\n",
      "Time:                        15:47:30   Log-likelihood                   -748.70\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      8.0209\n",
      "Entities:                         503   P-value                           0.0000\n",
      "Avg Obs:                       3.6203   Distribution:                  F(7,1308)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             5.9587\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       4   Distribution:                  F(7,1308)\n",
      "Avg Obs:                       455.25                                           \n",
      "Min Obs:                       428.00                                           \n",
      "Max Obs:                       496.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m8.0     0.0287     0.1170     0.2451     0.8064     -0.2009      0.2583\n",
      "treat_event_m6.0    -0.0942     0.1639    -0.5747     0.5656     -0.4158      0.2274\n",
      "treat_event_m5.0    -0.0798     0.0943    -0.8468     0.3973     -0.2648      0.1051\n",
      "treat_event_m3.0    -0.0871     0.1208    -0.7210     0.4711     -0.3240      0.1498\n",
      "treat_event_0.0      0.2373     0.0803     2.9567     0.0032      0.0799      0.3948\n",
      "treat_event_3.0      0.1426     0.0705     2.0226     0.0433      0.0043      0.2809\n",
      "treat_event_6.0      0.2152     0.0796     2.7047     0.0069      0.0591      0.3714\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.6161\n",
      "P-value: 0.0000\n",
      "Distribution: F(505,1308)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Malawi_cci.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 2.197  p-value: 0.700\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(3): 12.088  p-value: 0.007\n",
      "\n",
      "--- Running Placebo Test for Malawi - cci ---\n",
      "Treatment cohorts: {2013.0: np.int64(870), 2016.0: np.int64(219), 2019.0: np.int64(262)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Coefficient: -0.0470 (SE: 0.1060)  p=0.6579  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: -0.0351 (SE: 0.0882)  p=0.6907  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 2/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0          2013 -0.047009  0.105982  0.657940         56        157   \n",
      "1  2019.0          2016 -0.035143  0.088247  0.690706         69        157   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 2: Treatment defined by 'cash_sale' *****\n",
      "***** (Among CCI sellers: Never cash vs Late cash) *****\n",
      "**************************************************\n",
      "  Restricted to CCI sellers: 1850 households\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 1018\n",
      "  Note: This analysis is restricted to CCI sellers\n",
      "\n",
      "  Treatment assignment for cash_sale (among CCI sellers):\n",
      "  Control: CCI sellers who never sell cash crops\n",
      "  Treatment: CCI sellers who start selling cash crops after baseline\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 1018\n",
      "  Control group: 244 households\n",
      "  Treatment group: 317 households\n",
      "  Excluded: 457 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 561\n",
      "  Total observations at baseline: 561\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 244 households\n",
      "    Treatment (1): 317 households\n",
      "  Converting categorical variable 'head_sex' to numeric\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 244\n",
      "  Treatment households: 317\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 468/561 (83.4%)\n",
      "  Mean SMD: 0.099\n",
      "  Max SMD: 0.209\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 541/561 (96.4%)\n",
      "  Mean SMD: 0.118\n",
      "  Max SMD: 0.233\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 553/561 (98.6%)\n",
      "  Mean SMD: 0.132\n",
      "  Max SMD: 0.253\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 464/561 (82.7%)\n",
      "  Mean SMD: 0.078\n",
      "  Max SMD: 0.198\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 534/561 (95.2%)\n",
      "  Mean SMD: 0.124\n",
      "  Max SMD: 0.208\n",
      "\n",
      "kernel:\n",
      "  Matched: 561/561 (100.0%)\n",
      "  Mean SMD: 0.141\n",
      "  Max SMD: 0.272\n",
      "\n",
      "radius:\n",
      "  Matched: 561/561 (100.0%)\n",
      "  Mean SMD: 0.141\n",
      "  Max SMD: 0.272\n",
      "\n",
      "stratification:\n",
      "  Matched: 561/561 (100.0%)\n",
      "  Mean SMD: 0.141\n",
      "  Max SMD: 0.272\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Malawi: DID Results (from 'cash_sale' analysis among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Malawi - Cash Sale (CCI sellers: never cash vs late cash)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               537    0   537\n",
      "1.0               511  725  1236\n",
      "All              1048  725  1773\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.241839, unique_values=2\n",
      "  treatment_group: variance=0.211261, unique_values=2\n",
      "  post: variance=0.241839, unique_values=2\n",
      "  emp: variance=0.241746, unique_values=2\n",
      "  vharvest: variance=1588528283648.000000, unique_values=1666\n",
      "  lognf: variance=18.066862, unique_values=1003\n",
      "  educh: variance=0.171361, unique_values=2\n",
      "  head_age: variance=255.610909, unique_values=78\n",
      "  head_sex: categorical variable, unique_values=2\n",
      "  dependency_ratio: variance=0.990715, unique_values=38\n",
      "  farmsize: variance=0.604356, unique_values=589\n",
      "  asset: variance=2.910050, unique_values=243\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1773\n",
      "  Unique households: 464\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 537 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 511 obs\n",
      "     Treatment=1, Post=1: 725 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 1763/1773 (99.4%)\n",
      "  Mean: 0.592, Std: 0.492\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0377\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.9599\n",
      "No. Observations:                1733   R-squared (Within):               0.1030\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.5759\n",
      "Time:                        15:47:31   Log-likelihood                   -754.58\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      5.4710\n",
      "Entities:                         464   P-value                           0.0000\n",
      "Avg Obs:                       3.7349   Distribution:                  F(9,1257)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             79.572\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       4   Distribution:                  F(9,1257)\n",
      "Avg Obs:                       433.25                                           \n",
      "Min Obs:                       416.00                                           \n",
      "Max Obs:                       452.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.1401     0.0480     2.9210     0.0036      0.0460      0.2341\n",
      "treatment_group      0.8118     0.1625     4.9946     0.0000      0.4929      1.1307\n",
      "vharvest         -2.264e-08  1.449e-08    -1.5626     0.1184  -5.107e-08   5.786e-09\n",
      "lognf                0.0111     0.0039     2.8651     0.0042      0.0035      0.0187\n",
      "educh               -0.0431     0.0457    -0.9425     0.3461     -0.1328      0.0466\n",
      "head_age            -0.0040     0.0018    -2.2136     0.0270     -0.0076     -0.0005\n",
      "head_sex[Female]     0.1508     0.0531     2.8406     0.0046      0.0466      0.2549\n",
      "dependency_ratio -3.057e-05     0.0201    -0.0015     0.9988     -0.0394      0.0393\n",
      "farmsize             0.0592     0.0302     1.9636     0.0498   5.363e-05      0.1184\n",
      "asset               -0.0021     0.0166    -0.1244     0.9010     -0.0346      0.0304\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.4490\n",
      "P-value: 0.0000\n",
      "Distribution: F(466,1257)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Malawi - cash_sale ---\n",
      "Event times for treated units: [-8.0, -6.0, -5.0, -3.0, -2.0, 0.0, 3.0, 6.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m8.0 + treat_event_m6.0 + treat_event_m5.0 + treat_event_m3.0 + treat_event_0.0 + treat_event_3.0 + treat_event_6.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0154\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0642\n",
      "No. Observations:                1763   R-squared (Within):               0.0587\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.0623\n",
      "Time:                        15:47:31   Log-likelihood                   -792.32\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.8896\n",
      "Entities:                         464   P-value                           0.0053\n",
      "Avg Obs:                       3.7996   Distribution:                  F(7,1289)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             2.1426\n",
      "                                        P-value                           0.0368\n",
      "Time periods:                       4   Distribution:                  F(7,1289)\n",
      "Avg Obs:                       440.75                                           \n",
      "Min Obs:                       423.00                                           \n",
      "Max Obs:                       460.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m8.0    -0.0226     0.1124    -0.2015     0.8403     -0.2432      0.1979\n",
      "treat_event_m6.0    -0.0663     0.1493    -0.4437     0.6573     -0.3592      0.2267\n",
      "treat_event_m5.0    -0.1674     0.1108    -1.5102     0.1312     -0.3848      0.0500\n",
      "treat_event_m3.0    -0.1013     0.1187    -0.8535     0.3936     -0.3342      0.1316\n",
      "treat_event_0.0      0.1127     0.0820     1.3740     0.1697     -0.0482      0.2735\n",
      "treat_event_3.0      0.0796     0.0774     1.0281     0.3041     -0.0722      0.2313\n",
      "treat_event_6.0      0.0897     0.0839     1.0690     0.2853     -0.0749      0.2542\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.6452\n",
      "P-value: 0.0000\n",
      "Distribution: F(466,1289)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Malawi_cash_sale.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 2.702  p-value: 0.609\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(3): 2.103  p-value: 0.551\n",
      "\n",
      "--- Running Placebo Test for Malawi - cash_sale ---\n",
      "Treatment cohorts: {2013.0: np.int64(760), 2016.0: np.int64(156), 2019.0: np.int64(320)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Coefficient: 0.0645 (SE: 0.1230)  p=0.6003  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: -0.0592 (SE: 0.0827)  p=0.4741  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 2/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0          2013  0.064542  0.122956  0.600319         40        147   \n",
      "1  2019.0          2016 -0.059227  0.082673  0.474142         82        147   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 3: Treatment defined by 'cc' *****\n",
      "***** (Among CCI sellers: Never certified vs Late certified) *****\n",
      "**************************************************\n",
      "  Filtered to CCI sellers only: 1850 households\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 668\n",
      "\n",
      "  Treatment assignment for cc:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 668\n",
      "  Control group: 125 households\n",
      "  Treatment group: 112 households\n",
      "  Excluded: 431 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 237\n",
      "  Total observations at baseline: 237\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 125 households\n",
      "    Treatment (1): 112 households\n",
      "  Converting categorical variable 'head_sex' to numeric\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 125\n",
      "  Treatment households: 112\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 173/237 (73.0%)\n",
      "  Mean SMD: 0.085\n",
      "  Max SMD: 0.174\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 210/237 (88.6%)\n",
      "  Mean SMD: 0.112\n",
      "  Max SMD: 0.235\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 226/237 (95.4%)\n",
      "  Mean SMD: 0.133\n",
      "  Max SMD: 0.300\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 173/237 (73.0%)\n",
      "  Mean SMD: 0.095\n",
      "  Max SMD: 0.213\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 208/237 (87.8%)\n",
      "  Mean SMD: 0.099\n",
      "  Max SMD: 0.260\n",
      "\n",
      "kernel:\n",
      "  Matched: 237/237 (100.0%)\n",
      "  Mean SMD: 0.144\n",
      "  Max SMD: 0.338\n",
      "\n",
      "radius:\n",
      "  Matched: 237/237 (100.0%)\n",
      "  Mean SMD: 0.144\n",
      "  Max SMD: 0.338\n",
      "\n",
      "stratification:\n",
      "  Matched: 237/237 (100.0%)\n",
      "  Mean SMD: 0.144\n",
      "  Max SMD: 0.338\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Malawi: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Malawi - CC (CCI sellers: never certified vs late certified)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1  All\n",
      "treatment_group               \n",
      "0.0              122    0  122\n",
      "1.0              152  192  344\n",
      "All              274  192  466\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.242780, unique_values=2\n",
      "  treatment_group: variance=0.193678, unique_values=2\n",
      "  post: variance=0.242780, unique_values=2\n",
      "  emp: variance=0.221002, unique_values=2\n",
      "  vharvest: variance=205264125952.000000, unique_values=462\n",
      "  lognf: variance=22.284979, unique_values=313\n",
      "  educh: variance=0.135332, unique_values=2\n",
      "  head_age: variance=243.285630, unique_values=67\n",
      "  head_sex: categorical variable, unique_values=2\n",
      "  dependency_ratio: variance=0.916680, unique_values=26\n",
      "  farmsize: variance=0.513020, unique_values=271\n",
      "  asset: variance=2.142648, unique_values=98\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 466\n",
      "  Unique households: 173\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 122 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 152 obs\n",
      "     Treatment=1, Post=1: 192 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 466/466 (100.0%)\n",
      "  Mean: 0.672, Std: 0.470\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0600\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.9672\n",
      "No. Observations:                 463   R-squared (Within):               0.1142\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.0120\n",
      "Time:                        15:47:32   Log-likelihood                   -115.85\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      1.9709\n",
      "Entities:                         173   P-value                           0.0427\n",
      "Avg Obs:                       2.6763   Distribution:                   F(9,278)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             11.412\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       4   Distribution:                   F(9,278)\n",
      "Avg Obs:                       115.75                                           \n",
      "Min Obs:                       69.000                                           \n",
      "Max Obs:                       172.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0664     0.0946     0.7021     0.4832     -0.1198      0.2525\n",
      "treatment_group      1.0384     0.5187     2.0021     0.0462      0.0174      2.0594\n",
      "vharvest          -1.85e-09  1.041e-07    -0.0178     0.9858  -2.068e-07   2.031e-07\n",
      "lognf                0.0145     0.0080     1.8139     0.0708     -0.0012      0.0302\n",
      "educh               -0.0299     0.1488    -0.2012     0.8407     -0.3228      0.2629\n",
      "head_age            -0.0066     0.0062    -1.0612     0.2895     -0.0189      0.0057\n",
      "head_sex[Female]     0.1445     0.1463     0.9879     0.3241     -0.1434      0.4324\n",
      "dependency_ratio    -0.0115     0.0370    -0.3100     0.7568     -0.0844      0.0614\n",
      "farmsize             0.0988     0.0722     1.3675     0.1726     -0.0434      0.2410\n",
      "asset                0.0462     0.0407     1.1329     0.2582     -0.0341      0.1264\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.5574\n",
      "P-value: 0.0005\n",
      "Distribution: F(175,278)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Malawi - cc ---\n",
      "Event times for treated units: [-8.0, -6.0, -5.0, -3.0, -2.0, 0.0, 3.0, 6.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m8.0 + treat_event_m6.0 + treat_event_m5.0 + treat_event_m3.0 + treat_event_0.0 + treat_event_3.0 + treat_event_6.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0229\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.2266\n",
      "No. Observations:                 466   R-squared (Within):               0.1002\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.2329\n",
      "Time:                        15:47:32   Log-likelihood                   -129.05\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      0.9493\n",
      "Entities:                         173   P-value                           0.4688\n",
      "Avg Obs:                       2.6936   Distribution:                   F(7,283)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             0.9957\n",
      "                                        P-value                           0.4345\n",
      "Time periods:                       4   Distribution:                   F(7,283)\n",
      "Avg Obs:                       116.50                                           \n",
      "Min Obs:                       71.000                                           \n",
      "Max Obs:                       173.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m8.0     0.1534     0.2102     0.7296     0.4662     -0.2604      0.5671\n",
      "treat_event_m6.0     0.1649     0.3043     0.5417     0.5885     -0.4342      0.7639\n",
      "treat_event_m5.0     0.0370     0.2414     0.1534     0.8782     -0.4381      0.5122\n",
      "treat_event_m3.0     0.3012     0.2376     1.2678     0.2059     -0.1665      0.7689\n",
      "treat_event_0.0      0.1777     0.1758     1.0106     0.3131     -0.1684      0.5237\n",
      "treat_event_3.0      0.2932     0.1666     1.7604     0.0794     -0.0346      0.6211\n",
      "treat_event_6.0      0.1708     0.1730     0.9873     0.3243     -0.1697      0.5113\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.6569\n",
      "P-value: 0.0001\n",
      "Distribution: F(175,283)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Malawi_cc.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 3.030  p-value: 0.553\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(3): 3.354  p-value: 0.340\n",
      "\n",
      "--- Running Placebo Test for Malawi - cc ---\n",
      "Treatment cohorts: {2013.0: np.int64(182), 2016.0: np.int64(47), 2019.0: np.int64(115)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Coefficient: 0.3545 (SE: 0.2223)  p=0.1206  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: -0.1819 (SE: 0.1496)  p=0.2279  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 2/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0          2013  0.354497  0.222275  0.120574         14         61   \n",
      "1  2019.0          2016 -0.181937  0.149575  0.227877         41         61   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "============================================================\n",
      "********** NOW RUNNING: Ethiopia **********\n",
      "============================================================\n",
      "\n",
      "Ethiopia data loaded:\n",
      "  Observations: 11073\n",
      "  Time periods: [np.float64(2012.0), np.float64(2014.0), np.float64(2016.0), np.float64(2019.0)]\n",
      "  Number of households: 5976\n",
      "  'cc' variable found with 10362 non-missing values\n",
      "  Note: For Ethiopia, analysis is restricted to start from the second wave: 2014.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 1: Treatment defined by 'cci' *****\n",
      "***** (All households: Never sellers vs Late sellers) *****\n",
      "**************************************************\n",
      "\n",
      "  Filtering to households present at baseline year 2014.0\n",
      "  Households with baseline data: 3009\n",
      "\n",
      "  Treatment assignment for cci:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2014.0\n",
      "  Total households in data: 3009\n",
      "  Control group: 569 households\n",
      "  Treatment group: 320 households\n",
      "  Excluded: 2120 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 889\n",
      "  Total observations at baseline: 889\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 569 households\n",
      "    Treatment (1): 320 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 569\n",
      "  Treatment households: 320\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 531/889 (59.7%)\n",
      "  Mean SMD: 0.066\n",
      "  Max SMD: 0.193\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 714/889 (80.3%)\n",
      "  Mean SMD: 0.090\n",
      "  Max SMD: 0.174\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 796/889 (89.5%)\n",
      "  Mean SMD: 0.116\n",
      "  Max SMD: 0.204\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 525/889 (59.1%)\n",
      "  Mean SMD: 0.073\n",
      "  Max SMD: 0.215\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 717/889 (80.7%)\n",
      "  Mean SMD: 0.124\n",
      "  Max SMD: 0.278\n",
      "\n",
      "kernel:\n",
      "  Matched: 889/889 (100.0%)\n",
      "  Mean SMD: 0.156\n",
      "  Max SMD: 0.315\n",
      "\n",
      "radius:\n",
      "  Matched: 888/889 (99.9%)\n",
      "  Mean SMD: 0.151\n",
      "  Max SMD: 0.316\n",
      "\n",
      "stratification:\n",
      "  Matched: 889/889 (100.0%)\n",
      "  Mean SMD: 0.156\n",
      "  Max SMD: 0.315\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Ethiopia: DID Results (from 'cci' analysis) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Ethiopia - CCI (never sellers vs late sellers)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1   All\n",
      "treatment_group                \n",
      "0.0              378    0   378\n",
      "1.0              320  320   640\n",
      "All              698  320  1018\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.float64(2014.0), np.float64(2016.0)]\n",
      "  Treatment=1: [np.float64(2014.0), np.float64(2016.0)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.215743, unique_values=2\n",
      "  treatment_group: variance=0.233670, unique_values=2\n",
      "  post: variance=0.215743, unique_values=2\n",
      "  emp: variance=0.225827, unique_values=2\n",
      "  lognf: variance=18.091057, unique_values=358\n",
      "  educh: variance=0.228513, unique_values=2\n",
      "  head_age: variance=215.220184, unique_values=70\n",
      "  head_sex: variance=0.184448, unique_values=2\n",
      "  dependency_ratio: variance=3.545023, unique_values=11\n",
      "  vharvest: variance=1351773056.000000, unique_values=992\n",
      "  farmsize: variance=1.994977, unique_values=1016\n",
      "  asset: variance=1.441663, unique_values=635\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1018\n",
      "  Unique households: 531\n",
      "  Unique time periods: 2\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 378 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 320 obs\n",
      "     Treatment=1, Post=1: 320 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 1018/1018 (100.0%)\n",
      "  Mean: 0.344, Std: 0.475\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + lognf + educh + head_age + head_sex + dependency_ratio + vharvest + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.1488\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -2.9284\n",
      "No. Observations:                 972   R-squared (Within):               0.2704\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.4951\n",
      "Time:                        15:47:33   Log-likelihood                   -118.08\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      8.6626\n",
      "Entities:                         516   P-value                           0.0000\n",
      "Avg Obs:                       1.8837   Distribution:                   F(9,446)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             157.74\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       2   Distribution:                   F(9,446)\n",
      "Avg Obs:                       486.00                                           \n",
      "Min Obs:                       458.00                                           \n",
      "Max Obs:                       514.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.4289     0.0770     5.5711     0.0000      0.2776      0.5802\n",
      "treatment_group     -1.1523     0.6137    -1.8776     0.0611     -2.3584      0.0538\n",
      "lognf                0.0187     0.0094     1.9829     0.0480      0.0002      0.0372\n",
      "educh               -0.0142     0.0967    -0.1468     0.8834     -0.2042      0.1758\n",
      "head_age             0.0107     0.0078     1.3800     0.1683     -0.0045      0.0259\n",
      "head_sex             0.5080     0.1038     4.8921     0.0000      0.3039      0.7121\n",
      "dependency_ratio    -0.0105     0.0287    -0.3648     0.7154     -0.0670      0.0460\n",
      "vharvest          4.538e-07   4.87e-07     0.9318     0.3519  -5.033e-07   1.411e-06\n",
      "farmsize             0.0129     0.0293     0.4385     0.6612     -0.0448      0.0705\n",
      "asset                0.0319     0.0533     0.5989     0.5495     -0.0728      0.1366\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.1215\n",
      "P-value: 0.1057\n",
      "Distribution: F(516,446)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Ethiopia - cci ---\n",
      "Event times for treated units: [-2.0, 0.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_0.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.1303\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.3467\n",
      "No. Observations:                1018   R-squared (Within):               0.2561\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.3280\n",
      "Time:                        15:47:33   Log-likelihood                   -141.27\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      72.677\n",
      "Entities:                         531   P-value                           0.0000\n",
      "Avg Obs:                       1.9171   Distribution:                   F(1,485)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             40.654\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       2   Distribution:                   F(1,485)\n",
      "Avg Obs:                       509.00                                           \n",
      "Min Obs:                       487.00                                           \n",
      "Max Obs:                       531.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                \n",
      "===================================================================================\n",
      "                 Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-----------------------------------------------------------------------------------\n",
      "treat_event_0.0     0.4635     0.0727     6.3760     0.0000      0.3207      0.6064\n",
      "===================================================================================\n",
      "\n",
      "F-test for Poolability: 1.3435\n",
      "P-value: 0.0005\n",
      "Distribution: F(531,485)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Ethiopia_cci.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Only one or zero pre-treatment periods — joint pre-trend test not applicable.\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(1): 40.654  p-value: 0.000\n",
      "\n",
      "--- Running Placebo Test for Ethiopia - cci ---\n",
      "Detected 2 periods. Running baseline-difference placebo test (pre-treatment equivalence).\n",
      "  Baseline difference (future-treated vs control): -0.0433 (SE: 0.0373)  p=0.2455  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year     coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0        2014.0 -0.04332  0.037299  0.245463        320        211   \n",
      "\n",
      "                method  \n",
      "0  baseline-difference  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 2: Treatment defined by 'cash_sale' *****\n",
      "***** (Among CCI sellers: Never cash vs Late cash) *****\n",
      "**************************************************\n",
      "  Restricted to CCI sellers: 3694 households\n",
      "\n",
      "  Filtering to households present at baseline year 2014.0\n",
      "  Households with baseline data: 2440\n",
      "  Note: This analysis is restricted to CCI sellers\n",
      "\n",
      "  Treatment assignment for cash_sale (among CCI sellers):\n",
      "  Control: CCI sellers who never sell cash crops\n",
      "  Treatment: CCI sellers who start selling cash crops after baseline\n",
      "  Baseline year: 2014.0\n",
      "  Total households in data: 2440\n",
      "  Control group: 522 households\n",
      "  Treatment group: 352 households\n",
      "  Excluded: 1566 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 874\n",
      "  Total observations at baseline: 874\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 522 households\n",
      "    Treatment (1): 352 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 522\n",
      "  Treatment households: 352\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 550/874 (62.9%)\n",
      "  Mean SMD: 0.068\n",
      "  Max SMD: 0.270\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 732/874 (83.8%)\n",
      "  Mean SMD: 0.092\n",
      "  Max SMD: 0.389\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 793/874 (90.7%)\n",
      "  Mean SMD: 0.110\n",
      "  Max SMD: 0.441\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 575/874 (65.8%)\n",
      "  Mean SMD: 0.072\n",
      "  Max SMD: 0.254\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 753/874 (86.2%)\n",
      "  Mean SMD: 0.106\n",
      "  Max SMD: 0.352\n",
      "\n",
      "kernel:\n",
      "  Matched: 874/874 (100.0%)\n",
      "  Mean SMD: 0.190\n",
      "  Max SMD: 0.644\n",
      "\n",
      "radius:\n",
      "  Matched: 874/874 (100.0%)\n",
      "  Mean SMD: 0.190\n",
      "  Max SMD: 0.644\n",
      "\n",
      "stratification:\n",
      "  Matched: 874/874 (100.0%)\n",
      "  Mean SMD: 0.190\n",
      "  Max SMD: 0.644\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Ethiopia: DID Results (from 'cash_sale' analysis among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Ethiopia - Cash Sale (CCI sellers: never cash vs late cash)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1   All\n",
      "treatment_group                \n",
      "0.0              387    0   387\n",
      "1.0              352  352   704\n",
      "All              739  352  1091\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.float64(2014.0), np.float64(2016.0)]\n",
      "  Treatment=1: [np.float64(2014.0), np.float64(2016.0)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.218744, unique_values=2\n",
      "  treatment_group: variance=0.229104, unique_values=2\n",
      "  post: variance=0.218744, unique_values=2\n",
      "  emp: variance=0.249593, unique_values=2\n",
      "  vharvest: variance=2469004032.000000, unique_values=1084\n",
      "  lognf: variance=16.681463, unique_values=325\n",
      "  educh: variance=0.236773, unique_values=2\n",
      "  head_age: variance=201.313156, unique_values=72\n",
      "  head_sex: variance=0.168634, unique_values=2\n",
      "  dependency_ratio: variance=3.369312, unique_values=11\n",
      "  farmsize: variance=1.908161, unique_values=1090\n",
      "  asset: variance=1.130925, unique_values=612\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1091\n",
      "  Unique households: 550\n",
      "  Unique time periods: 2\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 387 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 352 obs\n",
      "     Treatment=1, Post=1: 352 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 1091/1091 (100.0%)\n",
      "  Mean: 0.475, Std: 0.500\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0425\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -5.6221\n",
      "No. Observations:                1049   R-squared (Within):               0.0725\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -2.8977\n",
      "Time:                        15:47:34   Log-likelihood                   -343.16\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.4911\n",
      "Entities:                         534   P-value                           0.0086\n",
      "Avg Obs:                       1.9644   Distribution:                   F(9,505)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             159.36\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       2   Distribution:                   F(9,505)\n",
      "Avg Obs:                       524.50                                           \n",
      "Min Obs:                       515.00                                           \n",
      "Max Obs:                       534.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.1579     0.0945     1.6711     0.0953     -0.0277      0.3436\n",
      "treatment_group     -1.5867     0.6191    -2.5628     0.0107     -2.8031     -0.3703\n",
      "vharvest          7.975e-07  6.178e-07     1.2909     0.1973  -4.163e-07   2.011e-06\n",
      "lognf                0.0061     0.0101     0.6039     0.5462     -0.0137      0.0258\n",
      "educh               -0.0572     0.1050    -0.5449     0.5861     -0.2636      0.1491\n",
      "head_age             0.0151     0.0081     1.8719     0.0618     -0.0007      0.0309\n",
      "head_sex             0.8955     0.1080     8.2910     0.0000      0.6833      1.1077\n",
      "dependency_ratio     0.0032     0.0337     0.0945     0.9248     -0.0631      0.0694\n",
      "farmsize             0.0474     0.0361     1.3104     0.1907     -0.0237      0.1184\n",
      "asset                0.0433     0.0510     0.8487     0.3964     -0.0569      0.1435\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.1287\n",
      "P-value: 0.0844\n",
      "Distribution: F(534,505)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Ethiopia - cash_sale ---\n",
      "Event times for treated units: [-2.0, 0.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_0.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0141\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.1340\n",
      "No. Observations:                1091   R-squared (Within):               0.0484\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.1134\n",
      "Time:                        15:47:34   Log-likelihood                   -376.55\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      7.6980\n",
      "Entities:                         550   P-value                           0.0057\n",
      "Avg Obs:                       1.9836   Distribution:                   F(1,539)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             3.5086\n",
      "                                        P-value                           0.0616\n",
      "Time periods:                       2   Distribution:                   F(1,539)\n",
      "Avg Obs:                       545.50                                           \n",
      "Min Obs:                       541.00                                           \n",
      "Max Obs:                       550.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                \n",
      "===================================================================================\n",
      "                 Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-----------------------------------------------------------------------------------\n",
      "treat_event_0.0     0.1720     0.0918     1.8731     0.0616     -0.0084      0.3524\n",
      "===================================================================================\n",
      "\n",
      "F-test for Poolability: 1.0759\n",
      "P-value: 0.1969\n",
      "Distribution: F(550,539)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Ethiopia_cash_sale.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Only one or zero pre-treatment periods — joint pre-trend test not applicable.\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(1): 3.509  p-value: 0.061\n",
      "\n",
      "--- Running Placebo Test for Ethiopia - cash_sale ---\n",
      "Detected 2 periods. Running baseline-difference placebo test (pre-treatment equivalence).\n",
      "  Baseline difference (future-treated vs control): -0.0919 (SE: 0.0440)  p=0.0367  ⚠️ FAIL\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 0/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0        2014.0 -0.091856  0.043964  0.036677        352        198   \n",
      "\n",
      "                method  \n",
      "0  baseline-difference  \n",
      "❌ Overall Result: 1/1 placebo tests failed. Suggests presence of pre-trends.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 3: Treatment defined by 'cc' *****\n",
      "***** (Among CCI sellers: Never certified vs Late certified) *****\n",
      "**************************************************\n",
      "  Filtered to CCI sellers only: 3694 households\n",
      "\n",
      "  Filtering to households present at baseline year 2014.0\n",
      "  Households with baseline data: 2120\n",
      "\n",
      "  Treatment assignment for cc:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2014.0\n",
      "  Total households in data: 2120\n",
      "  Control group: 415 households\n",
      "  Treatment group: 135 households\n",
      "  Excluded: 1570 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 550\n",
      "  Total observations at baseline: 550\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 415 households\n",
      "    Treatment (1): 135 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 415\n",
      "  Treatment households: 135\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 242/550 (44.0%)\n",
      "  Mean SMD: 0.091\n",
      "  Max SMD: 0.182\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 376/550 (68.4%)\n",
      "  Mean SMD: 0.062\n",
      "  Max SMD: 0.149\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 444/550 (80.7%)\n",
      "  Mean SMD: 0.068\n",
      "  Max SMD: 0.132\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 249/550 (45.3%)\n",
      "  Mean SMD: 0.053\n",
      "  Max SMD: 0.099\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 368/550 (66.9%)\n",
      "  Mean SMD: 0.056\n",
      "  Max SMD: 0.148\n",
      "\n",
      "kernel:\n",
      "  Matched: 550/550 (100.0%)\n",
      "  Mean SMD: 0.192\n",
      "  Max SMD: 0.585\n",
      "\n",
      "radius:\n",
      "  Matched: 548/550 (99.6%)\n",
      "  Mean SMD: 0.182\n",
      "  Max SMD: 0.598\n",
      "\n",
      "stratification:\n",
      "  Matched: 550/550 (100.0%)\n",
      "  Mean SMD: 0.192\n",
      "  Max SMD: 0.585\n",
      "\n",
      "Selected method: mahalanobis_3\n",
      "\n",
      "--- Ethiopia: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Ethiopia - CC (CCI sellers: never certified vs late certified)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1  All\n",
      "treatment_group               \n",
      "0.0              349    0  349\n",
      "1.0              135  135  270\n",
      "All              484  135  619\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.float64(2014.0), np.float64(2016.0)]\n",
      "  Treatment=1: [np.float64(2014.0), np.float64(2016.0)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.170805, unique_values=2\n",
      "  treatment_group: variance=0.246326, unique_values=2\n",
      "  post: variance=0.170805, unique_values=2\n",
      "  emp: variance=0.233041, unique_values=2\n",
      "  vharvest: variance=1176186112.000000, unique_values=619\n",
      "  lognf: variance=15.498590, unique_values=192\n",
      "  educh: variance=0.231295, unique_values=2\n",
      "  head_age: variance=160.786987, unique_values=61\n",
      "  head_sex: variance=0.147403, unique_values=2\n",
      "  dependency_ratio: variance=2.978387, unique_values=10\n",
      "  farmsize: variance=2.098057, unique_values=619\n",
      "  asset: variance=0.467871, unique_values=369\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 619\n",
      "  Unique households: 368\n",
      "  Unique time periods: 2\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 349 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 135 obs\n",
      "     Treatment=1, Post=1: 135 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 619/619 (100.0%)\n",
      "  Mean: 0.632, Std: 0.483\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0208\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.7327\n",
      "No. Observations:                 605   R-squared (Within):              -0.0070\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.5397\n",
      "Time:                        15:47:35   Log-likelihood                   -81.338\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      0.6244\n",
      "Entities:                         361   P-value                           0.7570\n",
      "Avg Obs:                       1.6759   Distribution:                   F(8,235)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             3.6748\n",
      "                                        P-value                           0.0005\n",
      "Time periods:                       2   Distribution:                   F(8,235)\n",
      "Avg Obs:                       302.50                                           \n",
      "Min Obs:                       245.00                                           \n",
      "Max Obs:                       360.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0581     0.1297     0.4479     0.6546     -0.1975      0.3137\n",
      "treatment_group      0.5845     2.2985     0.2543     0.7995     -3.9437      5.1127\n",
      "vharvest          1.118e-07  1.563e-06     0.0715     0.9430  -2.967e-06   3.191e-06\n",
      "lognf                0.0033     0.0148     0.2264     0.8210     -0.0257      0.0324\n",
      "educh                0.2018     0.1583     1.2749     0.2036     -0.1100      0.5137\n",
      "head_age             0.0047     0.0223     0.2102     0.8337     -0.0393      0.0487\n",
      "dependency_ratio     0.0176     0.0558     0.3156     0.7526     -0.0923      0.1276\n",
      "farmsize             0.0008     0.0412     0.0183     0.9854     -0.0805      0.0820\n",
      "asset               -0.0010     0.0846    -0.0117     0.9907     -0.1678      0.1658\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.4642\n",
      "P-value: 0.0008\n",
      "Distribution: F(361,235)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Ethiopia - cc ---\n",
      "Event times for treated units: [-2.0, 0.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_0.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0028\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0250\n",
      "No. Observations:                 619   R-squared (Within):              -0.0127\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.0250\n",
      "Time:                        15:47:35   Log-likelihood                   -91.993\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      0.7022\n",
      "Entities:                         368   P-value                           0.4028\n",
      "Avg Obs:                       1.6821   Distribution:                   F(1,249)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       2.0000   F-statistic (robust):             0.2868\n",
      "                                        P-value                           0.5927\n",
      "Time periods:                       2   Distribution:                   F(1,249)\n",
      "Avg Obs:                       309.50                                           \n",
      "Min Obs:                       251.00                                           \n",
      "Max Obs:                       368.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                \n",
      "===================================================================================\n",
      "                 Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-----------------------------------------------------------------------------------\n",
      "treat_event_0.0     0.0664     0.1240     0.5356     0.5927     -0.1778      0.3106\n",
      "===================================================================================\n",
      "\n",
      "F-test for Poolability: 1.3138\n",
      "P-value: 0.0102\n",
      "Distribution: F(368,249)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Ethiopia_cc.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Only one or zero pre-treatment periods — joint pre-trend test not applicable.\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(1): 0.287  p-value: 0.592\n",
      "\n",
      "--- Running Placebo Test for Ethiopia - cc ---\n",
      "Detected 2 periods. Running baseline-difference placebo test (pre-treatment equivalence).\n",
      "  Baseline difference (future-treated vs control): -0.0633 (SE: 0.0520)  p=0.2237  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2016.0        2014.0 -0.063297  0.052017  0.223658        135        233   \n",
      "\n",
      "                method  \n",
      "0  baseline-difference  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "============================================================\n",
      "********** NOW RUNNING: Uganda **********\n",
      "============================================================\n",
      "\n",
      "Uganda data loaded:\n",
      "  Observations: 11384\n",
      "  Time periods: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "  Number of households: 3788\n",
      "  'cc' variable found with 10506 non-missing values\n",
      "  Note: Uganda is NOT restricted to start from the second wave.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 1: Treatment defined by 'cci' *****\n",
      "***** (All households: Never sellers vs Late sellers) *****\n",
      "**************************************************\n",
      "\n",
      "  Filtering to households present at baseline year 2010\n",
      "  Households with baseline data: 2091\n",
      "\n",
      "  Treatment assignment for cci:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2010\n",
      "  Total households in data: 2091\n",
      "  Control group: 262 households\n",
      "  Treatment group: 390 households\n",
      "  Excluded: 1439 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 652\n",
      "  Total observations at baseline: 652\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 262 households\n",
      "    Treatment (1): 390 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 262\n",
      "  Treatment households: 390\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 551/652 (84.5%)\n",
      "  Mean SMD: 0.073\n",
      "  Max SMD: 0.150\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 638/652 (97.9%)\n",
      "  Mean SMD: 0.087\n",
      "  Max SMD: 0.161\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 652/652 (100.0%)\n",
      "  Mean SMD: 0.103\n",
      "  Max SMD: 0.252\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 564/652 (86.5%)\n",
      "  Mean SMD: 0.071\n",
      "  Max SMD: 0.165\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 631/652 (96.8%)\n",
      "  Mean SMD: 0.091\n",
      "  Max SMD: 0.193\n",
      "\n",
      "kernel:\n",
      "  Matched: 652/652 (100.0%)\n",
      "  Mean SMD: 0.103\n",
      "  Max SMD: 0.252\n",
      "\n",
      "radius:\n",
      "  Matched: 651/652 (99.8%)\n",
      "  Mean SMD: 0.100\n",
      "  Max SMD: 0.249\n",
      "\n",
      "stratification:\n",
      "  Matched: 652/652 (100.0%)\n",
      "  Mean SMD: 0.103\n",
      "  Max SMD: 0.252\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Uganda: DID Results (from 'cci' analysis) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Uganda - CCI (never sellers vs late sellers)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               470    0   470\n",
      "1.0               547  970  1517\n",
      "All              1017  970  1987\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.249986, unique_values=2\n",
      "  treatment_group: variance=0.180678, unique_values=2\n",
      "  post: variance=0.249986, unique_values=2\n",
      "  emp: variance=0.157364, unique_values=2\n",
      "  lognf: variance=19.885176, unique_values=973\n",
      "  educh: variance=0.177186, unique_values=2\n",
      "  head_age: variance=259.877655, unique_values=74\n",
      "  head_sex: variance=0.231975, unique_values=2\n",
      "  dependency_ratio: variance=3.001874, unique_values=76\n",
      "  vharvest: variance=1534868717568.000000, unique_values=431\n",
      "  farmsize: variance=10.020006, unique_values=667\n",
      "  asset: variance=1.803001, unique_values=290\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1987\n",
      "  Unique households: 564\n",
      "  Unique time periods: 5\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 470 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 547 obs\n",
      "     Treatment=1, Post=1: 970 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 1729/1987 (87.0%)\n",
      "  Mean: 0.805, Std: 0.397\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + lognf + educh + head_age + head_sex + dependency_ratio + vharvest + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0162\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.6188\n",
      "No. Observations:                1660   R-squared (Within):               0.0045\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.4343\n",
      "Time:                        15:47:37   Log-likelihood                   -228.59\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      1.9932\n",
      "Entities:                         555   P-value                           0.0369\n",
      "Avg Obs:                       2.9910   Distribution:                  F(9,1092)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             2862.3\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       5   Distribution:                  F(9,1092)\n",
      "Avg Obs:                       332.00                                           \n",
      "Min Obs:                       228.00                                           \n",
      "Max Obs:                       543.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post          -0.0754     0.0467    -1.6153     0.1065     -0.1669      0.0162\n",
      "treatment_group      0.8357     0.1359     6.1491     0.0000      0.5690      1.1024\n",
      "lognf                0.0042     0.0031     1.3618     0.1735     -0.0018      0.0102\n",
      "educh                0.0796     0.0594     1.3408     0.1803     -0.0369      0.1962\n",
      "head_age             0.0021     0.0018     1.1631     0.2450     -0.0014      0.0056\n",
      "head_sex            -0.0642     0.0472    -1.3596     0.1742     -0.1569      0.0285\n",
      "dependency_ratio    -0.0053     0.0082    -0.6443     0.5195     -0.0215      0.0109\n",
      "vharvest           -5.8e-09   7.64e-09    -0.7591     0.4480  -2.079e-08   9.192e-09\n",
      "farmsize             0.0050     0.0051     0.9955     0.3197     -0.0049      0.0150\n",
      "asset               -0.0182     0.0151    -1.2089     0.2270     -0.0478      0.0114\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 2.1293\n",
      "P-value: 0.0000\n",
      "Distribution: F(558,1092)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Uganda - cci ---\n",
      "Event times for treated units: [-10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m10.0 + treat_event_m8.0 + treat_event_m6.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_4.0 + treat_event_6.0 + treat_event_8.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0278\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.1373\n",
      "No. Observations:                1729   R-squared (Within):              -0.0397\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.1829\n",
      "Time:                        15:47:37   Log-likelihood                   -256.32\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      3.6843\n",
      "Entities:                         558   P-value                           0.0001\n",
      "Avg Obs:                       3.0986   Distribution:                  F(9,1158)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             2.4524\n",
      "                                        P-value                           0.0091\n",
      "Time periods:                       5   Distribution:                  F(9,1158)\n",
      "Avg Obs:                       345.80                                           \n",
      "Min Obs:                       232.00                                           \n",
      "Max Obs:                       552.00                                           \n",
      "                                                                                \n",
      "                                 Parameter Estimates                                 \n",
      "=====================================================================================\n",
      "                   Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-------------------------------------------------------------------------------------\n",
      "treat_event_m10.0    -0.1766     0.1740    -1.0149     0.3104     -0.5180      0.1648\n",
      "treat_event_m8.0     -0.0432     0.1420    -0.3045     0.7608     -0.3218      0.2354\n",
      "treat_event_m6.0     -0.0440     0.0804    -0.5463     0.5850     -0.2018      0.1139\n",
      "treat_event_m4.0     -0.1509     0.0674    -2.2389     0.0254     -0.2831     -0.0187\n",
      "treat_event_0.0      -0.0680     0.0483    -1.4081     0.1594     -0.1628      0.0268\n",
      "treat_event_2.0      -0.1757     0.0607    -2.8961     0.0038     -0.2947     -0.0567\n",
      "treat_event_4.0      -0.1723     0.0677    -2.5440     0.0111     -0.3051     -0.0394\n",
      "treat_event_6.0      -0.1736     0.0833    -2.0846     0.0373     -0.3370     -0.0102\n",
      "treat_event_8.0      -0.2176     0.0795    -2.7369     0.0063     -0.3735     -0.0616\n",
      "=====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.9718\n",
      "P-value: 0.0000\n",
      "Distribution: F(561,1158)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Uganda_cci.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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x6/1pI4GOO9alkfxpo4I+locffticA50JXRM3Tfj0PGtCbU2qpbQcfvr06fL++++bXlP9v+kYc/0bmkTq/WnvtI6P15mts0r/nv7vdSiA9rxq44I2Cmg82rCiSbJ/ybj+/zQWnZRMz78mqdpooTN56/ADnQBOE2jtodbnVW574IEHzGPXSci08UjHeFvnQ/+3/jN767nXZbb0OanPNW2o0WoIfR3pY9SZ3gEA6dHTDADIFZoo6MRSZ7oEjlH1L9HWL/na0xeMJtOaIFo9rNrTqD2r2nOqCUNOl0zSpE/LwDXp1ARpx44dGR6vSe2sWbNMsqclvZqoaUw6TliTZR0zrNdrMu3PSpA1IdRlqrSH1Cpxnjhxohl/qr1+mhBqkqljpzUhDqRJjiZ0el+aqOvf1vHK2htvlY+HYl2fG6XZFi1N1l5yTbw0YdZkTJfd0oRMk0WdRdzqeQ+kS3Jp3Ho7Tfa0EUDPqT4+PU/+ayvrufjoo4/MedLngSb1+ty75pprzPPAei5lNtkPRhP3KVOmmOea1RCgsU2ePDldT6wm0R9//LFp2NBJyvTv6nNHqwt0ffGbb745aM94btGqDI1VJyPTc6V/X5N06+/7Dw3QRoSpU6eaxFl7vvVYrQrQ15Aeq88pAEB6UV4GrwAAUGBogq09wdqjqr3X4ZphPLO0tF1j0l5tHZ/rT0u0NXnWyd+0Bx4AgEigpxkAgAJAxxVrz/yrr75qehd14qpIJ8zWBGDao6vjkf3b8TVWLZPWfaGqDwAAyAv0NAMAUADojNtaPqyTRel61rpWs44BjjTtTdZJxLTUWsepa/m1Jsxatq7jwrUsWsuxrQnUAADIa/Q0AwBQAOgEYNqzrOOZ33nnHVskzNaYXB1nqxOw6YRpCxcuNBNa6bhwnYiLhBkAEGn0NAMAAAAAEAI9zQAAAAAAhEDSDAAAYFMUBAJA5MVEOgAAADLjm2++MTMsz54926yNq+NeBwwYEPRYXbqoVKlSZp1jXYO2d+/eYhe6rrKuA63r4upSSkpnh9b1oXV94rJly2brfhs0aGDWuV61alXIY6ZNmyYPPvhgpu9Tl4HKyXrH4abjoHX2bR33rBOd5ZSuj63rLWeFLtulE5iFw9dff22eK88++6xvn65Frb/r60EndAMAhB9JMwDA9nQW5aeeekoGDx5sEmZ/5cqVk44dO6bZl5KSYmZj1kmlfv31V/n999/l4YcfloKuZs2acumll6bZd/DgQVm0aFHQ85jdBN6ptNEh8PyoGTNmmJ/BrtPbhMOyZcvMRGgXXHBBmv060/ikSZNM48fEiRPD8rcBAGmRNAMAbE/X69Xe40GDBqW7rm7duvLyyy8HvZ0mzbfccovpidSEp1mzZlKQtWnTxlz8aY+9Js0ZnceCQhsJgp0DK2nOy/Pj8XiC7tcZ0O+9914ZOHCgzJw501ZVFACQXzGmGQBga+vXrzfJwbXXXpvlXr3zzjvPV26rZbRAftChQweznvVrr71mqioAAOFFTzMAwNbee+89MxlS3759s3V7a7zp4cOH0+xfsWKFKXNduXKlHDp0yKwFXLt2bVP+quOOXS5XmvHCrVq1MmNotUw8Pj7e9MxOmTLF3E7Hwr7//vumZ3vfvn2ml1DHm3bt2lVuu+02sxZxdmhcb775pkn4tURd7+f888+X4cOHm/HGecEa56s99Tpu+PXXX5fjx49L69atzf9GnThxQt59912ZNWuW7N69W4oVKybt2rWTO+64w5w7fzp+WxM9HZs+btw481PPpz4e/R9rNUFMTNqvJzreW4/VUvuEhAQzFlx7W0PRdZ91zPg///wjiYmJUqNGDenRo4e5b40tt33//ffy4Ycfytq1a8XtdsvZZ58t1113nXk82jPsb/ny5Wad7HXr1pnnZIUKFUxZvD63NE41atQomT59utnWMdt6Dq+88kp54YUXfPej9/3MM8/IvHnz5JJLLsn1xwQA+Bc9zQAA2zp9+rTMnTtX6tev70sosmrz5s3mp/+kSToh1g033GASjlq1aplE7qyzzpK//vpLnnvuOXMJpMngf//7X1PCqwlh1apVTcKsPeGa0Hz88ccSFxcnnTt3lhYtWpjjP/jgA7n55ptNIpVVO3bsMJOYaWm5JpE6tlUTLI1d92vSlZf++OMPeeyxx6ROnTqmzF0bGJQmfpogjh8/3vy/NKnX67777ju55pprTKIbSJNmTWA/+eQTc3+ajGti/Morr5hE0N/WrVtNlYE+bm000POribw2bFj/W3+aXD/00EOyceNGad68uUlIddy27h86dGiuz0b96quvmsaB1atXS+PGjU0v8JYtW8wY+sBx9DrR23/+8x/55ZdfzPNOG1X0OfTFF19Iv379TIOLatmypW98eaVKlUyDhe7zZ4111onBAABh5gUAwKZ+/fVXb/369b1PPPFEuut+++03c92NN94Y8vYzZswwxzRq1Mj7zz//mH0nT570tm7d2tuiRQvvunXr0hw/f/58c3yzZs28SUlJvv26Ty8PPPCAb5/b7TY/b775ZnPd559/nua+du7c6T333HPNdcuXL/ft13h13+rVq337unbtavYdPHjQt69fv35m35tvvun1eDy+/VOnTjX7e/bs6U1JSUkToz6mrDrTedyxY4fv8Y8ZMybd47/77rvNdY8//rg3MTHRd/3PP//sbdq0qbd9+/beY8eOpXusPXr08G7fvt23f+nSpd6GDRt6Gzdu7E1ISPDtHzRokDn+tdde8+3Tv2P9Xb3oY7D2N2/e3PzN+Ph43/HHjx/3XnrppebYRYsWZfkcWX8n0IIFC3z/i23btvn26//xqquuMtd9/fXXvv0DBgxIF4P+bx988MF0j9H6v9xyyy0h4+rcubP5n/s/VwEAuY/ybACAbekMwkp7mkP5+++/05Xqakmu9jRqL6W65557TI+m0lLgLl26mBJaXZLKn+7X2bl15m3tQa1YsWKa67V32qLl29prqeXfOhmT9qr603LjTp06maWB9uzZk6XHrSXjOuN327ZtTXm3P+1l1nJgLdnWHsvA2ZXDKfDx63nS8mrtNdVeVZ2szaI9ztoDrT3leg78b6u0d9a/ekAfq/6ftede/29NmzY1vc/aU6091yNGjPAdq72zWiavpctaGm45duyYnDp1yvRIlyxZ0rdfS7J1uTK9X401t1izVz/xxBNmZnKLViNoj/kVV1xhyrYvu+wys19L7K3eY4uWb+tja9KkiekZzwo9Xz///LMpC8/qbQEAmUfSDACwLU3KlJZCh6Klt9bsxpYiRYqYJZR0rKeW9mrJrEUTNS0DDiwX3rZtm/z5558m4VbJycnp/lbg+FxNeDR586eJtJbZrlmzxpe0B7uvjOjavCrU2sOajGvSrMflVdKspeGBS1Bpo4aO39bx3v4Js3+cmjRrnIFJc7Akr3z58uanJr7W+F+lpcr+Y8xV8eLFzfnxX0da/+eaYOt513JnHZ+uDSHaYKJjsPWSW7TkXsfFa+l8sPvVibo0Hk1oNbHXxF1nLtdx1rq+uI5J1v+dngcdOhB4fjLDGteurxOSZgAIH5JmAIBt6URYVoIUio4v1gm9skITW006dTzohg0bzPhjaxZia+KmwLGvmohrD2cwOhZax+fquNbt27ebsb0Z3VdmGwt0HK5eQrHGwOYF/57bwDh10ipr4qrMxlmiRIl0+6wJwKzllg4cOJCuZ9Zf4GRoer51jLFOlKbJql6ef/5530RgN954Y4YNMFlx5MgR3/9Ze8Uzoo9Dk2atiNCEXhsR3nrrLXPRXnEd26y98joWPiusc6gNRwCA8CFpBgDYlpXIhlqzNrs9hFryrGWthQsXNjMx69JU2ousCfidd94ZdIKpwJ5OiyY+mqhpwqbl3tq7raXfOnHT119/bWbYzirr8WoPbkazZJ8pWctNwR6/Fac+7nr16oW8bXZn+g6ceTpQ4CzbSifjmjNnjvn/ai+0Tr6lk6rpTN/asKGTswVOqpUd1uRumgzrRHIZsRpbSpUqZRp4Vq1aZSah09nWtdHGanTQCcx0orCsxpCdieYAAJlH0gwAsC1NMoItF5UTOr5WEypNnHQ5pzJlyqS5XsfFZpYmY7oEk5bh6pJTgWOkNUnLbim0uuiii2Tw4MFiV1acWp6sM2vnNmtMuVYCBGP1RAdLUvXc6UVt2rTJ/J80UdWee2uprJzQHmItSdcqgpdffjlLt9XnnpW462PQ54nOPq7rLvfv3z9oqXsw1uvCep0AAMKDJacAALZlTa4UKjnK7tJJSsc6BybMmlxZkzVlpqRax0Brb6uOmw1MmJOSknxjcrPaU26NkdWJvoLRJE2XuQocy53XrDi1NzfYuO3Jkyeb5ZK0dzc7dMyy9jYvWLAg3f3r+dW/G/i/7dWrV7oEXnvB77//frOd1UnZQtHEXKsUTp486fs/B5ak9+zZ0yxzpbHqOG2dLM6aFMy/4UGXMtMJ6PS+rEabM/WyK+u5ai3/BQAID5JmAIBtWWM8rUQ3N2hyojQR809mdSzyyJEjfb9bE4Jl5r40aTp+/Lhvv25rkmaN+c3MffnTics00dOkUHvD/ePUXnKdtVlnmdakLZJ0JmpdN1knt3r22WdNcug/zlt7d3UW88AGhaz0NGviqedRZ6P2L0fW3wPH8tatW9es4azVBDq+3N+3335rfubmOdO1otUjjzxi1ma2aIKss4nr+GVtmNEEW9fw1nJyLcfWNb0DJ1TTBFjL2LVqQenQAZWQkBD0b2ujjjba6Fj77J5fAEDmUJ4NALAt7WksWrRo0J687NJlgLSUWpdKWrdunRnLrMmXjjPVcbvau60JtPZu69jkjOiMxZrY6/JQ3bt3NyW3mjjqklE6Y7LeXsdH6zJXWaG9jDrD98CBA03J7hdffGFmY7bitJY5skMPoyavOsHWp59+apbC0nHW+th1ZmlNbrWn1X/28qzShFQbCD777DPTiKDnQf9vuhyVLtOkyblFJ4x78MEHzbnRSgIdE65JqCa0mrzrtv/SVTmlvdpLliwxsWkPcrNmzUyptP6PdBI7/f/rOGX/x6Ll1zrjut5GZ/XW4/RcWddbdCmz6Ohoc92QIUPMzNv+y49pVcTRo0flwgsvDDlBHQAgd9DTDACwLU2YL774YpMgaeKUG7R3WHv6NNnQ5E4ni9Kxyfp3vvzyS7n55pvNcT/99NMZ70uTGp0ITJcL0li1F1h7/zR50jGqY8aMyfR9BdJkXmf3tpYi0p5xPQ/as6vLOF1//fViBzqztZ63W265xUyKpZNbaUOBlm7r+OHANbSzSsuXNSHXXl1tkNBzqTN5v/3220Fnm9bzohOz6d/X54z+f/X/rLNT62RbmozmpieffFJGjx5tYtG/t2jRIpOc6zrUmhjr2GeLJvlasq4NLNoAojO46zrj+lzUCeP8JxTT+9D71tm+f/vtN3O//nR8ttKlqwAA4RXlzeo6GAAA5CEt/e3du7dJhsIx2RTgNFqur5OcaSOFlqJnZvwzACD76GkGANjaWWedZZJmTQ78xw0DBZVVdXD77beTMANAHiBpBgDY3n333WdKoSdMmBDpUICI9zJr+XmnTp3MmuAAgPAjaQYA2J6Om9XS7A8//DDXlgwCnEjHZetr4Lnnnot0KABQYDCmGQAAAACAEOhpBgAAAAAgBJJmAAAAAABCIGkGAAAAACAEkmYAAAAAAEKICXUFcofOs+bx2HeuNV3e0UlTwRFv+DktZuINL+INP6fFTLzhRbzh5bR4nRgz8YYX8eYelysq02vdkzSHmSbMhw6dEDvS50hsbLQkJ7tt+2T2R7zh57SYiTe8iDf8nBYz8YYX8YaX0+J1YszEG17Em7vKli0m0dGZS5opzwYAAAAAIAR6mgEAQMSkuD3y21/7zPa5TSpJTDTt+QAAeyFpBgAAERV/9FSkQwAAICSSZgAAENGJWNo2rOjbBgDAbkiaAQBAxLiioqRaheKRDgMAgJAYOAQAAAAAQAj0NAMAgIjxeL1y+Fii2S5TsrDpeQbyE7c7RTweTw7WuHVJSorHlkv2BCLe8CLejLlcLomODk96S9IMAAAixuPxyi+rd5vtPh1riyuTa2YCdnfq1Ak5ceKYpKQkRToUoMCIiSkkxYqVlLi4Yrl7v7l6bwAAAFlULC420iEAuZ4wHz0aL4UKxUnp0hUkOjpa+91y0FsnjkG84UW8oXjF7XbLyZPHzWtP5WbiTNIMAAAiRtdl7t6mRqTDAHKV9jBrwlymTAWJyuGQA5Kk8CLe/BNvbKxI4cJxcvjwAfMazM2kmYnAAAAAgFwcw6wl2UWLFs9xwgwga/Q1V7RoMfMa1NdibiFpBgAAAHKJNelXakk2gLxmTQaW3Qn4gqE8GwAARIzb45Gl6/ab7XaNKkq0i/Z85Bf0MgP55bVH0gwAACJGx7rtO3TStw0AgN2QNAMAgIjRdZlb1a/g2wYAwG5ImgEAQMS4XFFSs1KJSIcBAEBIJM0AAAAAcsV7702Q999/J1PHtmjRSsaNe1siIT7+gCxd+pv07n2pOEFCQoLMmzdH+va9JsPjZs2aIc8996Tcffd90rfvtRIpnTq1kQYNGsn770+S/ICkGcihuUu3y3fLdmT5dj3a1pCe7WqGJSYAcAqP1ysJJ5LMdolihSjRBhyuZcvWcvPNaffNnv2t7N27R6655nopXry4b3+VKlXzPkAROXz4kPTvf7W0a9feMUlz//59pWLFSmdMmu3i5puHSvnyqUNv8gOSZiCHTiWmyOGExGzdDgAKOo/HKz+t2mW2+3SsLa5okmbAyVq1aiMtW7ZJs2/VqhUmae7X7/qIJcr+Tp8+LSdPnhAn0URfk2anGDz4VslPSJqBHIorHCNlShROs8/r9cqR46k9J6WLFzILrQe7HQBApEgh3g8BAPbFpxSQQ1piHVhmnZjklmGjF5jtF27tIIVioyMUHQDYW0y0Sy5uz1AVoKCyxuA++eTz8s0302T16t+lXLnyMn78u6ZnVcfyfvTR+zJ//o8SH79fypQpK507X2DKf0uVKp3mvnbv3iWTJk2U5cuXmDHLMTGxUrt2Henbt59cfHHvNH9P6X2ed14beeihx6VXr0vNONw+fS6XHj0ukXfeGS8bN26QEiVKymWXXWn+3qZNG+SNN16Xv/76U0qWLCXdunWXoUOHSeHC/3aeeDwemT79C5kx42vZvn2bFClSxIzdHjLkVjnrrLPTPe7XXhsvGzask2++mS779+8zj1ljuOGG/4jL5ZKVK5fLnXfeZm6jx2m8GsuZenLdbo8ZXz5jxleSkHBM6tVrIAMGDJKOHTulO1bHSn/xxWfyzz+bJTo6Wpo0OUcGDhwizZq1SHfsV19NlWnTPpddu3ZK2bLl5YorrpKyZcvJs88+IWPGvGUqDYKNadbrtUx/1qwfZcKEcfLLLwvk+PHjctZZdU1c+j/19/nnn8icObNkx47tZsLI+vUbyrXX9pdOnbpIJLgi8lcBAACAAirF7TEXrUzzH6qg+9weT54e6/E/NoKLpb/22v9MEnX11ddJkyZNfQnzsGGD5NNPJ0mNGjXMmGhNxKZO/VxuvXWQHD16xHd7TeKGDBkg8+bNlmbNmsu1194g5513vvz99yZ55pnH5fvv55rj6tWrb+5HaUI9aNBQs8+iCfE994wwieAVV/Q11YLvv/+2vPLKC3LHHUMlNjZWrrrqGpMof/bZZJk8eWKax/HUU4/Kq6/+z/wPNKHUZHDZsiVy6603y5o1q9M97vHjx8jEie+ZxFrv9/TpUzJhwhvy8ccfmuu1nF2TZKWNCRqvjhs/k48/nmji0yRZGwG2bv1HHnjgbpMg+9O/9eSTj5hz2bv3ZdK9+yWyceN6GTHiVlmw4Mc0x44Z84q8/PLzkpSUJJdddpU0bXqOvP32ePngg8xN/Kbuuut2MwFbt24XSffuPU2i/vDD98mff/7hO0YbPsaMGW229X+gDR5bt26RUaPukQULfpJIoKcZAAAAyEPfLtpqfl7SvpYULpRajbZp5xFZt+2w1KpcQlrW+3cCpdm/bTfJrU4gWrRIrNm3Zc8x+fOfg1K9QnFp07Ci79h5y3ZIYrJburWqLiWLFTL7tu9PkN83xUuVcsWkfeN/x8T+uHKXnDydLF1aVPMNM9t14ITUqPjvRF15SZPR8ePfkcKFi/j2aY9karL0qOl9tXz33Rx56qlHTML54IOPmX2TJ38ox44dTdPbqTRBGzlyuEkWL7qop+lx7devhHzxxacmadYeW/+2gi1b/kkz83SPHr1k8OAbTQ/rkCG3mR5YpT3BV17ZW+bNm+vr9f3hh+9Mcq691g888IjptVX9+98kQ4b8R5555gn55JMvTQ+yZc+e3TJx4idSrVp187s2Glx//VUyffqXctNNN5ukWe9fE1OdWCsw3lCOHj0q77zzoXm86vrrb5RbbrlZXn/9ZenSpZsUKlRI1qz5UyZN+kDatTtXnn/+FV+P+c03D5GhQ/8jzz//tLRt216KFi0m69evNb3R55zTXEaPHidxcXHm2O7dL5b7778r0/9n/buTJn1ueuBV69btzP9Sz6/et/rss0nmfLz99kSJiUlNV/v1628mQ9Ne7i5dukpeo6cZAABEjPZ+LV23z1wCe8IAFBwdOpyXJmFOSUmRuXNnmV5g/4RZ9ehxsZx9dn2ToOpxSnsjtczaP2FWVq/s4cOHMxWHJo6XX97X93uDBg2lUKHCpsf5uutu9O3X0vBq1arJvn17fPtmzvzGHDd8+N2+hFnVrFnb9OLu3Lld/vwzbW9z164X+hJmpUly7dpnmVJt67FlR8+evXwJsxXD5ZdfJUeOHDE932rWrG/Mz2HD7kxTYq492tddd4McP55gyqjVnDkzTe/5rbfe4UuYlfZka2KdWVoqbyXM1v9d7d69M011xJEjh025vUXP0aefTpP//e91iQR6mgEAQMRoj8nu+NRZbFvVzz/LkwAZ0ZniVbTr34lC61UvLXWrlZLAuUMvObemeZ34H1unSknTIx14bPe2NdLdb82KJUyPdOCx3VpVMz91vKilWoViEilVq6bGY9HxwKdOnZLk5BQzNjeQ1+uRxMRE2b59qxkr3Lx5C3PRMuNNmzbKzp07zHVWkqpjjTOjcuUqvt5NS1xcESlRoniaZE9pMp2cnOz7XcuaNfnUXuxAVlK4efMGE6elRo1a6Y61luXS+w6MJbOaNm2Wbl/jxk3+P4aNpnRdx2yrH3+cJz//nLbs2UpY9Vz27NlL1q1ba35v1Cj1PvxpD7GViJ9J4OP997H+20Cgyb2Wvd944zXSuHFTOffcjiY513HNkULSDAAAIkbXZW5Wt7xvGygoE+AF0uTVJVFBjw0sx83o2Kzcb7pjI/gaDExItZdT6VjcDz74J+TtdNyz0tLs119/xfQ+u91u0+OrvZNt2rSTdev+SjPOO+M4/u1F9Rcbm1runhGNRf92RmN8rXgthQqlltwHk9mYg9EJ0wIVLVrU/NTGCP9zrCXaoeM9Zn5qY4QmuP490pasrMcc+HitFWb8H6v2Zuv/7uuvp5kx5joW/N133zI98A888LCvjDsvkTQDAICI0S/0Z1UtGekwANhMXFxqgqfjg7Xs+kyefvoxWbx4oSn/1cmsdFZmTRJ10iodL5tXMets21988bVE2okTx9Pti4+PNz9Llkx9z9Uya+3J/uGHhWnKyYMpWrSYGX+tjQKBx544kbtrXmsifemlV5jLwYPxsnz5Upk//wdTKv7AAyNl6tRv05SI5wXGNAMAAACwlZo1a5lJozZsWB/0ep1dWpeiOnnypOm91YRZl0q6++77zazOVq/qtm1b0vVkWr2bue3ss+uZMc7Bxk/rTNTaW7ptW+okcOGmpeKBrNm7rTLnunXrmXHTmzdvSnfsqlUrzMzaa9euMb/rrOWaMOuyV4GsY3LDsWPHTDm+Lk9lja/W8nCdqEyX+NKKgi1b/pa8RtIMAAAiRr/IHj+VbC45KUUEkL9oGbAmSbpk1Oefpx0j/Ntvi+Stt8aZ2ao1OdaZt3VGak2o/CfPOnnyhCnZVm73v/utccJJSf+OR84Nl1zSx4ydfvXVl9LEsXfvXnn55RfMOF1rDG9WaczJyUmZPn727JmmZ9g/idZEVCcasyZH0158aykpbXywaCOELi2lZdsuV2qvcu/eqcdqIn369GnfsbqOdOB46JzQ/+eXX06Rd9550yTQFj2vOjma/p91ObK8Rnk2AACIGLfHK98v3+GbHCkmmnHNAFLdccddZv1eTep++WW+NGzYWPbt22uSNE2q77vvYd946M6du5oS3ltu+Y8Zx6wlw7/+usAkgMWKFTNLMPnPfK292CtWLJWxY181t23W7N/JubJLk1D9mzqxlib7bdueaxLdH3/83owNvuuue03PaXZUqFDRLIelyfe5555nJvLKiJZgDxlyk1kSShPin3763uzXJbqs8mqdaVyXuPryy8/kppv6mZmsNTnXtZAPHNhv1rpu2LCRb2IxXTNZS90HDuxvJuc6fPigzJ//o5QoUcLMyn2mEu/M0L8/ZMitZq3rAQOuNetc64RrK1cuMxOXXXHF1VkaQ51b8k3SrK05kydPlilTpsiuXbukYsWKcuWVV8ott9xiWp/OZP78+fLee+/JmjVrTOlBnTp15Prrr5frrrsuT+IHAKCgio2h8A1AemXKlDFr9WoZ9s8/LzDlxTrBlSa5AwcONrNmWx588FGTTGly/eWXn0v58uWlWbOW8p//DJbPPptslkz655/N5jaaG4wceb+8++4EmTbtCylWrHiuJM1a9v3MMy/J1Kmfy6xZM+Sbb6abmbfr1j1b+vcfYGaAzi4tO3/ttf/Jt99+bWbVPlPSPGzYCDNz+Jw535qJv5o2bS633XZHutmvNZHXZbU0GdZzpEmrLk+lk3FpWXRgDFWrVpcZM6bL119PlfLlK8odd/zXjJX+5JOP0iwZlhO6Rnbp0mVMMq/VBNqzreX6un62Js2REOXNJ7VQjz32mEmY27ZtKy1atJCVK1fKihUrpGfPnjJmzJgMb/vZZ5/J448/bl6YF110kWm5+umnn0zyfdNNN8kjjzyS7bjcbo8cOpS7g+Nziw7niI2NluRkd6YWSY80J8WbmOSWYaNT17V7654uUig25y1vecFJ51gRb3gRb/g5LWbiDS/izR/xas/iwYN7pFy5KpmacTkzcTvh/FqIN3/Ge/BgvGlsKFmyVLrrnnnmcZNwz5jxXbpZuyMRb2Zfg2XLFpPoILPIB5MvmnY1QdaEuXfv3qa3+d5775WPP/5YrrjiCpk7d64sWJCavASjtfIvvviiVKhQQWbMmCHPPPOMPProo2a7UaNGMmnSJFm3Lv2AdwAAAAAoCObOnS29el3om6DLsmvXTvn55/lmOahgy1zlF/kiadYEWd1xxx1pyiNGjhxpfk6dGnqa+V9//dXU+ffv398kzhYd+3DzzTeb7V9++SWs8QMAAACAXV10UQ8pXryEvPTSs/Loo6PMRGzPPfekDBp0g6SkJJty9/wsX4xpXr58uUl469atm2Z/pUqVpHbt2rJ06dKQtz3rrLPk7rvvls6dO6e7zlq82382OQAAkHvcHo/8sfmg2W5+djmJduWL9nwAyFcqVqwk7777kZlRW2fMXrjwZ5NE62RnN910sxkXnZ85PmnWBct1GvdWrVoFvb5atWqyZcsWM2NeqVLpa/AbNmxoLsF8/33qLHP16tXL5agBAIDSsW7b9yWY7WZ1y0U6HABACNWr1zCzbxdEjk+adXpza1r1YHQKdKXTzQdLmjPqvZ45c6aZea9bt245ijFM66fnmBWXXeNzcrz+Meq2E2J22jlWxBtexBt+Tos5HPFGu6Kk6Vllfdu5ed+c3/Ai3oz/DoDIys3v4I5Pmq2Fw3WttWCs/YmJiZm+z02bNpnx0bqI9pNPPilxcXE5nqnRjjQ2XU/NKbMGOilet198MbHRtn0OOPkcK+INL+INP6fFHK54G9cJTw8z5ze8iDc4r9eVq1/YdX4eEQec4P9HvOFFvGdmve5iYlwZfgfPyuvT8UmzLmaudL2yUOXbqmjRopm6v7/++kuGDBlierB1rLMuQZUT+qasSxvYkfWhkZLinKUinBJvit//XLedMkLPSedYEW94EW/4OS1m4g0v4s0f8aakeMxP/Ru583e8jji//yLe8CLeM7H+nr4Wo6JC52FZicvxSXPx4sXF5XKZ8utgrP1WmXZGFi1aJMOHD5cTJ07InXfeKbfddluuxGj3J3buvannDSfE6x+fE+IN5LSYiTe8iDf8nBZzbsbr9XrldFLql5oihbQXMPdrWwvy+c0LxJv+/gHkr9e645NmLb+uWrWq7Ny5M+j1ul9n1tbkOiO6nvM999xjyr0ffvhhGTBgQJgiBgAAFrfHK3OXbjfbfTrWlphoBoQCAOzF8Umzat26tXz99deyY8cOqVGjhm//vn37ZOvWrdK9e/cz9jBrwqxjmF988UW5/PLL8yBqAACgXMycBJyRNi59t2xHlm/Xo20N6dmuZlhiAgqKfJE0X3HFFSZpHj16tLloaZeWe+m26tevX8jb6tjle++914yJfumll0iYAQDIQzHRLrmsU51IhwHY3qnEFDmckJit2wHImXyRNHfs2FF69eols2bNkt27d0u7du1kxYoV5tK7d2/p3LmzOe7YsWPy4YcfmvHNAwcONPsmT54sBw8elIoVK8r27dtl7Nix6e6/ZcuW0qlTpzx/XAAAAICKKxwjZUoUTrNPO4mOHE+d9LZ08UJB5wTQ2wHImXzzKtKy6rp168r06dNl4sSJZpzzXXfdJYMHD/Ydo0nzuHHjpFq1ar6kWddjVvv37zfXBTNo0CCSZgAAAESMllgHllknJrll2OgFZvv5WzpI4ULOWOIScJp8kzTrhGA687VeQqlevbps2LAhzT5NsAEAQGS4PR5Z888hs930rLIS7XLKIn0AQnnvvQnywQfvpNuv62RrxWe9eg2kb99rpVOn1GrQrJo1a4Y899yTcvfd95n7yY7vvpstzZq1kCpVqkgkWH+/cuUqufaYckpXHZo3b45cddU1Efn7dpZvkmYAAOA8uhzIlj3HzHaTOmUjHQ7gGMkpblmydp/v9wnf/CVtGlaQtg0rSmyMPXqczz+/i5x9dn3f77pKTXz8Afnxx3mybNkSuffeB+WKK/pm+X7r1asvN988VBo1apKtuMaPHyOffPKRfPTRZxIJwf5+Th9Tbujfv69UrFiJpDkIkmYAABDRmbMb1izj2wZwZqs2HZD3Zq6Tk6f/neTrj7/j5ffN8fLJ95tkSO/G0qJeeYm088+/QHr1ujTd/ssuu1Juv32IvPnmGLn44t5SpEiRLN2v9lTrJbsOH06tbomUYH8/p48pt+LSpBnpUQMFAAAixuWKkoa1ypiLbgM4c8I8buqfaRJmq2pD6f6xU1eb4+zqnHOaS9OmzeTEiROyevXvkQ4HOCN6mgEAAACHlGRrD/P/58ch6fV63KvDy9qmVDuQrlyjjh494tuXmJhoypZ1XO2ePbulaNGi0rJlGxk0aKicddbZvuOCjf/t1KmNXHJJH+nT5wp5553xsn79WomNLSTt2rWXYcPu9I0dvvrqS2Xv3j1me8CA68z+L7+ckWGs27dvk/fff1uWL18qJ0+ekKpVq0vv3pdJv37Xm3HaFr3u7bfflCVLFsm+fXulWLHi0rx5S1N2Xbfu2Rn+/VCPqU+fy6VHj0vMY9q4cYOUKFHS9NTrfW7atEHeeON1+euvP6VkyVLSrVt3GTp0mBQuXDhN7/HkyRNl0aJfZd++faZxslq1GtKrVx/p16+/mXF95crlcuedt5njN2xYZ/6u3v/gwbeaffHxB8zj1/vQ/1eFChXloot6yoABg7JcJeBU9DQDAICI0SVzNBHQi24DCG3Z+v3pephD0eOWr7dvb/POnTvNT03AVGLiabnrrmFmErFChQrLFVdcbRLmX39dILfcMlBWrVpxxvvUhE/vIzo6xozL1UT1hx/myT33jPC9v2iia42z1r+hv2dEk++hQwfIzz//JG3atJOrr75O4uKKyBtvvCaPPTYqzfvWo48+KF9++ZnUqFHLJKTt2p0rixb9IsOGDZa9e/dm6+9rQqzxly1bzoz/1iRXE9hXXnlB7rhjqMTGxprHqonyZ59NNgmy/8pBeu6mTv1c6tatZ/6WJtbaIDF27KsyefKH5rgqVaqaJFmVK1febLds2dr8rscOGTJAvv32a2ncuKl5XFWqVJOPPnpf7rrrdklKSl3yLL+jpxkAAESM2+OVmYu3me0+HWtLTDQl2kAoqzbGiw79z0z7kh63cuMB6dC0stjNr7/+bBLccuXKSZMm55h9H3/8kfz552q5/PKrZOTIB3w9uH/8scr0gj799GMyZcpXJkkM5Z9//pYRI+6Wa6+9wfyuCa0mnEuX/mZ6U1u3bmuSvk2bNsrmzRvlqquuNol1qPOpt3/mmSfE7XbL229/KGefXc933UsvPSvffDNd5s6dZcZl//PPZtPDrNuPPPKk7zhNtJ999gmZM+dbGThwSLq/79+DHsyWLf+k6X3u2bOXDBp0o3z11VQZMuQ2c5/qhhv+I1de2VvmzZvr6yH+6qsvTdL70EOPpxlbrsfeeOM18v33c+SmmwaapFlvozOely9fwXd79fLLL8ihQwfllVfGSNu25/r2a9L89tvjTWWAFUN+RtIMAAAAOMDx08mZSpiVHqfHR9Ivv8w3SZslOTnZJLaaXGpSPHLkKF8SPHv2TClatJiMGDEyTcmzljdrwjdjxlcm+T3vvPND/r24uLg0yzVpr+y553Y0t9u1a6dJmrNCe3m3bv3n/3uH/02Y1W23DTcxadyaKHs8Xl8p94kTx01pttIyZu21ze4EW9qDfPnl/84w3qBBQ9MTn5ycJNddd6Nvf6lSpaVatWqyc+cO3z597Lpfy7v91apV2/RcHz58OMO/rWXZ+r86//wL0iTMqn//ATJlyscye3ZqY0B+R9IMAAAiJtoVJZedV8dsM3k2kLHiRWKz1NOsx0fSL78sMBeLJshlypSVzp27mkRUJwSzxgLv2bPLJJfBxsjqesaaoGrvbEZJs/aYxsSkTW+s5FUT9qzasGG9+bljx3ZTNh4oLq6obN68wWxrj7VObrZmzWq57LKeJkFv376jnHdeZ9946uzQ2wY+Ji0PL1GieLpzlZpM//s469dvaC6axK9Zs9Ek1JrUr127xox1LlmyZIZ/W8dQqyNHDgV9/IULFzGNEfr/0waP/IykGQAARIz2BJEsA5nTsn55WbExc+OUNbFuVb+CRFJgWXAoOou2KlYseOJVvnzq8lmnT5/O8H504q9g7zGpsj5nwvHjCebn4sULzSUYl8vl+zujR48144R1IjOdNEsvr732P2nXroM8+OCjpvQ5q4oUicv0Yw2kE6u9+eZYU0aelJRo9lWqVNk0Tvz99+YzziNhPf4//1xtLqEkJCSQNMP55i7dLt8t+7dUI7N6tK0hPdvVDEtMAAAAyJq2DSuadZgzMxlY0SIx0qZhZJPmzNJZslV8fHzQ648dS03eSpUqladxaU+yevjhJ8zM3GeiieMtt9xuLtu2bZUlSxabMc9a4qzjml999Q3JS+PGvSbTp38hF17YQ668MnX8tNW7fPnlPSUlJePnkZa7q8GDb/VNFFZQMXt2AXAqMUUOJyRm+aK3AwAgnHQc4JotB83FGhMIIDhdPmpI78ZypuIMvV6Ps+tyU4G0hFpnZN62bYscO3Y03fV//LHS/KxTp26u/L1/e58zZo1j1knLAuls32PGvGJ6cZX23KYu/7TGN25YS9AnTPjAzBDuvx51Zv9+TmmPt86G/eSTz0mLFq18CbMuG6Xjmc9U5q8zblsziAfSXuoJE96QTz6ZVCBWPiBpLgDiCsdImRKF01xKF/+3pEO3A6/Xi94OAIBw8ni9snnnUXPRbQAZa1GvvAzve47pSfZn5WG6f0TfZuY4J7n44l5y6tQpk3jqbNWWP/74Xb799huTeGZ1Iq9QdEkqlZSU8ThnnYRMk3lNjHUcsL8PPnhXPv/8U/n7703mdx1L/Omnk+TDD99Lk0QmJBwz5edaFp3Vv59ThQsXMiXax48f9+3TOEePflE8Hk+6nmYdO60TjFmqVq1mku2FC3+Rn3+en+bYr7+eJpMmfSC//74yzxoBIomsqADQEuvAMuvEJLcMG506McMLt3aQQrHOaIkEAOQvrqgoObt6Kd82gDNrWa+CvDq8rCxes08mzkmdrKp53fKmfFtLsp3Sw+zvxhsHmnLmmTO/MRNwtWrVWg4cOGBm4NYJxB599Kl0E2JlV4UKqWXr2lOsaymHmv1ZZ/HW5aPuuWe43H77EDOLdJUqVWTt2r9MslizZi25+eZbzLENGzaSCy7oJvPn/yiDB98orVq1NQnoggU/mYmyBg9+OMt/P6d69OhlEvkhQ26STp26SEpKshlnvXfvHlPqrmORdZ3lQoVSO9O0YUKXuNJlpjp0OM9Munb//Q/J7bcPlYcfvs/sq127jmzdusWM8dZJ3e68c6QUBPQ0AwCAiHG5oqRpnXLmotsAMkcT4/aN/13G6NbLmpg1mZ2YMFtLK40Z85YZO6sTfk2f/qVZo7lbt+5mjeRWrdrk2t+66qp+0rZte1m/fp188cWnpoc7lObNW5i/36VLV1m1aoV8+eUUsxSTrgX9xhvvSunSpX3HamJ/663DTQ+u9sTOmTPTlGm/8spYM644O38/J3Rs9aBBqUn9tGlfmJnMNenV86wxaG/zsmVLfMfffff9ZgbymTO/No0VqmbN2vLuu5OkT5/LzfrSX3zxmUmse/e+TN5550OpXr2GFARR3oJQhB5BbrdHDh1KnRHQTvx7mt+6p4sjepq1AyI2NlqSk92ZXqMwUpx4fp12jhXxhhfxhp/TYibe8CLe/BGv9i4ePLhHypWrkqkZjs8koyWm/L9vvDmyixQuFPnvG5ldEssuiDf/xZucyddg2bLFJDo6c33IlGcDAICI0bZ76wuVfrkqCGPjgNxaDcW/7+vBtxcHff2wGgqQcyTNAAAgYtwer3y7aKvZ7tOxtsREkzQDGa2GEsqR40khbwcgZ0iaAQAAAIeshpKd2wHIGV5FAAAgYqJdUdK7Qy3fNoDMr4YCIG+QNAMAgIjRMZhOne0XAFAwsOQUAAAAAAAh0NMMAAAixuPxysYdR8x2/RqlWasZ+YiD1gUC8hVvrt8jPc0AACBiPF6vrN9+2Fx0G3A6lyv167Xb7Y50KECB5HanpHkt5gZ6mgEAQMTosrJ1qpT0bQNOFx0dIzExheTkyeNSuHAca48DeUjXLj958oR5DeprMbeQNAMAgIiJdrmk+dnlIx0GkKuKFSspR4/Gy+HDB6Ro0WL//+U9e8mz5txOKsIg3vAi3lC8podZE+akpFNSqlTufq6QNAMAAAC5KC6umPl54sQxOXIkPtLhAAVGTEwhkzBbr8Fcu99cvTcAAAAA5ku7XrT3y+PxZLuXLibGJSkpHkf0LhJveBFvxnQMc26WZPsjaQYAABGT4vbIrMXbzHavDrUkJpo5SpG/6Jf46OjsJx2xsdESFeV2TJJEvOFDvJFD0gwAACKKWbMBAHZG0gwAACIm2hUlPdvV9G0DAGA3JM0AACBidDmeuMJ8HQEA2BcDhwAAAAAACIGmXQAAEDEej1f+3n3UbNetWkpclGgDAGyGpBkAAER0ErC/thwy23WqlBSXkDQDAOyFpBkAAER0SZKalUr4tgEAsBuSZgAAEDHRLpe0ql8h0mEAABASE4EBAAAAABACPc0AbG/u0u3y3bIdWb5dj7Y1fOu/AgAAAAW6pzklJUUmTpwol1xyiTRr1kwuuugieeONNyQ5OTnL9zV58mRp0KCBnDhxIiyxAsiaU4kpcjghMcsXvR0Ae0txe2Tm4q3motsAANhNvulpfuqpp2TKlCnStm1bufDCC2XlypUyZswY2bBhg/mZWcuWLZP//e9/YY0VQNbEFY6RMiUKp9nn9XrlyPEks126eCGJCjKDkN4OgP0lp5AsAwDsK198o9QEWRPm3r17y+jRo31fqEeNGiVfffWVLFiwQLp06XLG+5k5c6Y8/PDDcvr06TyIGkBmaYl1YJl1YpJbho1eYLZfuLWDFIqNjlB0AHIi2hUlF7Wp4dsGAMBu8kV59scff2x+3nHHHb592us0cuRI83Pq1KkZ3v7QoUPmtnp82bJlpVatWmGPGQAApH5eF4+LNZdgFSMAAERavkialy9fLhUqVJC6deum2V+pUiWpXbu2LF26NMPbb9q0SX744Qe56qqrTM+03g4AAAAAAMeXZyclJcnevXulVatWQa+vVq2abNmyRY4ePSqlSpUKekzNmjXl66+/NpN/AQCAvOPxeGXr3gSzXbtyCXFRog0AsBnHJ81HjhwxP0uWLBn0+hIlSpifCQkJIZPmKlWqmEu42LHazD8m3bZjjIGsGJ0Uq7XthJgV5zi8nHR+FfGGn9NiDke8XvHKn//Em+1alYvnaok25ze8iDf8nBYz8YYX8UZOTH5YakoVKlQo6PXW/sTERIkEfZLE2nCCIrf33+2Y2GhbxhjsXEZHR5ufXr/47ciJ51dxjsPLSedXEW/4OS3mcMTrio6SGpVSG7gLFYqWaFfujRzj/IYX8Yaf02Im3vAi3tyVlWTe8UlzkSJFzM9Q6zFr+bYqWrSoRII+QZKT3WI3KX4x6bYTBrdbL7iUFLctX3hOP7+KcxxeTjq/injDz2kxhyve1vUrmJ8et1c87tz7zOT8hhfxhp/TYibe8CLe3JWVmByfNBcvXlxcLpcpvw7G2m+VaUeC3Z8kum3HGENxQrxOPr9OidnJ55h4w8tp8ToxZuINL+INL6fF68SYiTe8iDfvOaFzJkNafl21alXZuXNn0Ot1v86srck1AAAAAAAFKmlWrVu3NjNo79ixI83+ffv2ydatW6VFixYRiw0AAISW4vbInCXbzUW3AQCwm3yRNF9xxRXm5+jRo8X7/33/+lN/V/369YtofAAAILTTSSnmAgCAHTl+TLPq2LGj9OrVS2bNmiW7d++Wdu3ayYoVK8yld+/e0rlzZ3PcsWPH5MMPPzTjmwcOHBjpsAEAKPB0XeauLav5tgEAsJt8kTSrF198UerWrSvTp0+XiRMnmnHOd911lwwePNh3jCbN48aNk2rVqpE0AwBgA66oKClVvHCkwwAAIP8nzToh2PDhw80llOrVq8uGDRvOeF+TJk3K5egAAAAAAE6Ub5JmAADgPB6PV3YeOG62q1fQZSQp0QYA2AtJMwAAiBiP1ysrNx4w21XLFxOXkDQDAOyFpBkAAERMVJRIpbJFfdsAANgNSTMAAIiYaJdLOjSpHOkwAADI3+s0AwAAAAAQDiTNAAAAAACEQHk2AACImBS3R35atctsd21ZTWKiac8HANgLSTMAAIioE6eSIx0CAAAhkTQDAICI0XWZz29W1bcNAIDdkDQDAICIcUVFSblSRSIdBgAAITFwCAAAAACAEOhpBgAAEePxemVP/AmzXaV8MdPzDACAndDTDAAAIsbj8cqy9fvNRbcBALAbepoBAEBElS8VF+kQAAAIiaQZAABEjK7L3KlZlUiHAQBASJRnAwAAAAAQAkkzAAAAAAAhUJ4NAAAiJsXtkV9W7zHb5zerYsq1AQCwE5JmAAAQUUePJ0Y6BAAAQiJpBgAAEeNyRUnHppV92wAA2A1JMwAAiBhXVJRULFM00mEAABASA4cAAAAAAAiBnmYAABAxHq9X9h8+ZbYrlokzPc8AANgJPc0AACBiPB6v/PbXXnPRbQAA7IaeZgAAEFGlSxSOdAgAAIRE0gwAACJG12W+oEW1SIcBAEBIlGcDAAAAABACSTMAAAAAACFQng0AACImxe2RRWv2mu2OTSubcm0AAOyEpBkAAETUoWOnIx0CAAAhkTQDAICIcbmipH3jSr7t7Ji7dLt8t2xHlm/Xo20N6dmuZrb+JgCg4CBpBgAAEeOKipIq5Yrl6D5OJabI4YTEbN0OAIAzIWkGAACOFlc4RsoErPXs9XrlyPEks126eCGJiooKejsAAM6ETwsAABAxHq9XDh1NHdNctlQR0/OcVVpiHVhmnZjklmGjF5jtF27tIIVio3MpYgBAQcMUlQAAIGI8Hq/8+ucec9FtAADshp5mAAAQUSWKFop0CAAAhETSDAAAIkbXZb6wdfVIhwEAQEiUZwMAAAAAkN+T5pSUFJk4caJccskl0qxZM7nooovkjTfekOTk5Ezdfs+ePXLfffdJp06dpGXLltK/f39ZtGhR2OMGAAAAANhXvinPfuqpp2TKlCnStm1bufDCC2XlypUyZswY2bBhg/mZkfj4eJMk689LL71UihcvLjNnzpRBgwaZxFvvDwCA3JKc4pZl6/fLqk3xcuJ0ihQrEiMt65WXtg0rSmxMwZrlOcXtkSVr95nt9o0rmXJtAADsJF8kzZoga8Lcu3dvGT16tG99xlGjRslXX30lCxYskC5duoS8/euvvy67d++Wd955Rzp37mz2DR48WPr27WuS8fPPP18KFWKSEgBAzq3adEDem7lOTp5OEV1dyesV83PFhgPyyfebZEjvxtKiXnkpSA4cORXpEAAACClfNOd+/PHH5ucdd9zh2xcVFSUjR440P6dOnRrytidOnDCJdfPmzX0Js6pUqZLcdNNNsnfvXlm4cGGYHwEAoKAkzOOm/mkSZqUJs/9P3T926mpzXEHhckVJ6wYVzUW3AQCwm3yRNC9fvlwqVKggdevWTbNfE9/atWvL0qVLQ9529erVkpSUJO3bt093nbUvo9sDAJDZkmztYT7TSsR6vR6nxxcErqgoqVGxuLnoNgAAduP48mxNeLU3uFWrVkGvr1atmmzZskWOHj0qpUqVSnf99u3bzc+aNWsGva3aunVrtuPT3gMdrxWMfjeIdv3bbhHqOIv/OK+cHhvq9nkZQ6hj3R6Pr9cl8HxFBfRChDrWEu2KMtUG4TzW4/GKJ+Bg/8erQwUswY71p70s1pfGSBxrnpMx//4v9Dg9PuT9RkX5eoby+thgzyk91+5M3u+ZjvV/febWsXqcKzrtczij10Yk3yNCveZ4j8j+e8Rva/f5epjPRI/Tcb7nNqmcZj/vEZk/lveI9HiPsPd7RDB6SIz/a47vEbl6rInX77nDe0TBeo/wnqkV2//24nBHjhwxP0uWLBn0+hIlSpifCQkJQZNm6/bWccFue/z48WzHdzIxWWYuDp50VypTVDo0/fcL0Zwl280/MZjypeKkU7Mqvt/nLd8hScnBeyFKFy8sF7RMTfjVTyt3mTj8pbi9aZ/Q//98/vmP3ZJwMino/RYtHCs92tXw/b7wzz1y5Hhi0GMLxUZLr3Nr+X7XL3/xR4OPWdMX86Xn1fb9vmzdftl3+GTQY10ul1ze6d9jV248ILvjT0gofTrWlpj/f7Gu/vugbN+XEPLYS9rXksKFUifg+WvLIdmy51jIY3u0rSFFi8Sa7XXbDsnmXUdDnt/jp5KlSOHUl9qmnUdk/fbDIe+3S4tqUqZEYbP9z56jJo5QOp1TRcqXjjPb2/YlyOq/40Mee27jylK5XFGzvSv+uDlvoXRoWkUql029373xJ8xkRaG0ql9BalZKfZ0cOHRKflu7N+SxzeqWl7Oqpr5ODx89Lb/+uSfksU3qlJV61Uub7WPHk2TB77vSHRPsOZxwMll+XLkz5P2eXa2UND2rnNk+lZgi3y3bEfLYOlVKSvOzU8eVJiV7ZPaSbSGP1XOg50LpB12o17yqUamEtGmQeqzK6NhIvkf4ji1RRLq2rOr7nfeI7L1HTJ63UfYdytq43fdnrZeZv22TcxtXytfvEfoF8XSSWxrVKivNzi5nvpTyHsF7REH/HpHm2Pa1pFjh1Pvle0TufY/wHXtWOalfPTVH4D2iYL1H9OvRUErGpL5e8n3SrEtNqVATdVn7ExODn3BrSapgtz/TbTMjSrRFKngVvLbExcb+O0tqdHSUeENUzGsrWJpjXS5xubyZOzY6fQwu778vmJjYaN/xetuQ8UZHBdxvBse6AmPIwv3GBD829UtP6rFWy1BG96v0WKtl6UzHZvY8BB4bExOd+fMb4rH5jvV7TsRER2chhjPF68ow3sAWYuscZ3SsdV+++43NwmOLPcP9Rp/5sQU7x2e8X/943Z4Mj/V/Hel378weqy2qGZ7f/z/Weg5neL8RfI+w4tUW9cy+5niPCH6sPu9SUjJuAQ9Fb+f/d/Lje4T2AG3fd1SSUjzSskEF8//gPYL3iIL2HpHRc0L/vhUz3yNy73uEL16/88t7RMF6j4iSzA8JivL614460KFDh6RDhw7StWtXeeutt9Jdf9ddd8ns2bNl/vz5UqXKv60nlnfffVf+97//mWWpevbsmea606dPmwnCzj33XPnwww+z/YUn/mCC7UomEpPcMvy1X8z2hHu7mNaavI4hO2VV+malV1rX27Wsyv/8vnVPZylcKMYxZVWFCsWIx536+O1cVhXsOeyEsirzweH59zls97KqwNcc7xHZe494feofsnpz6B6fUJqdXVZGXNUsX79H6P9+/qpd5svLhW2qm/817xG8RxS094iMXnP6HcJtveb4HpGrx5p4Y2PE4yvX5T2iIL1HlC9XIs3whwxvLw6nayprS4GWXwdj7Q9Wfq2sku1gtz/TbTMj8MkayP89J6PjcvvYaL+WI/+a/ryMIdSxriiXdtGHbG1NTnb/27oW4thg9x2uY/VDLzpg8hr/86t3lNGxWbnfcB9rWgSjokyLqFkGR6LMB3um7jePjw3+HM78/UbiWOv9INntttVrLtSxwV5zdojXie8Rd13dQhat2SPvfrtOMmton8Zpyury63uE/u97tvu3xC737pf3iHAfy3tE7hx7ptecXm89h/kekbvHWj2hbjfvEQXxPSIqC3NPZi61tjEtoa5atars3Bl8/IHu15m1NbkORmfXto4LdltVp06dXI0ZAFDwtG1YUYoWyVxbtR7XpuG/Y9YAAEDkOD5pVq1btzYzaO/YkXYw/r59+8zM1y1atAh52yZNmkiRIkVk2bJl6a6zlprK6PYAAGRGbEy0DOnd+IwjqPR6PU6PBwAAkef48mx1xRVXyNdffy2jR482l9QyFq/ZVv369Qt526JFi0r37t1lxowZZtzzBRdc4Eu4J02aZMZBd+7cOc8eCwAg/2pRr7wM73uOWYdZl5XS0rDUksvUn9rDrAmzHldQ6Niy5etTZ+LV3vUzldgBQFbNXbo9w5muM5rlvGe79MvSouDJF0lzx44dpVevXjJr1izZvXu3tGvXTlasWGEuvXv39iW9x44dMxN66RjlgQMH+m4/cuRIWbhwoQwfPlz69Oljlq+aOXOmHD58WMaPHy+xsalLAgAAkFMt61WQV4eXNYniyk0HTPKsyXKrehVM0ljQepi1sWDPwdQlf7xeStIB5D5dHupwQmK2bgfkm6RZvfjii1K3bl2ZPn26TJw40Yxz1pmzBw8e7DtGk+Zx48ZJtWrV0iTNeuyUKVPk5Zdflh9++EHcbrc0atTI/K4zcwMAkJs0MdZJvjqeU9nMhOo/SUpBo5MGWT3r1uy8AJCb4grH+NavtmhV6pHjqesFly5eyDebeeDtAJVvngk6IZj2FOsllOrVq8uGDRuCXlezZk2z7BQAAMg7OnNt7colIx0GgHxMS6wDy6x1WbphoxeY7Rdu7eBbfhXI10kzACB7GOsFAAAQGkkzABRwjPVCJGmJZMLJZLNdomhs0BJJAAAcnTQnJyfLmjVrZP/+/RIdHS2VK1c2yzjxoQcAzsBYL0SS2+OVH1fuNNt9OtaWmGi+PwAA7CXb33iOHDkiY8eONRNvnTp1Ks11ZcqUkRtuuEGGDh1qxhoDAOyLsV6ItMI8vwAA+S1p1oT5xhtvlL///luKFCkinTp1MusZezwes+STLvWks1QvXbpU3n33XZZsAgAAQcVEu+SSc2tFOgwAAHI3aX7zzTdl8+bN0rNnT3nmmWfMuseBSfWoUaNkwYIFZl3kIUOGZOfPAAAAAAAQUa7s3GjevHlmrWNdxzgwYValS5eW119/XSpWrCjTpk3LjTgBAAAAAHBG0nzw4EFp2rRphmXXhQsXlhYtWphybQAAgGDcHo8sX7/fXHQbAIB8kTSfddZZsn79ejOGOSPbtm2TGjVqZDc2wJGSU9yyZO0+3+8TvvlLFq3ZY/YDANLyekV2HjhuLroNAEC+SJrvvPNO2b59uzzxxBNmyalg3n77bdmwYYPcdtttOY0RcIxVmw7I3eMWysQ56337ft8cL+9+u87s/31TfETjyy9omADyD1dUlJxzVjlz0W0AAPLFRGC6JnPnzp3liy++kF9//VW6desmNWvWNOs063W//PKLrFu3TipVqiSrVq0yF3+PPPJIbsUP2CphHjf1TwnsKLF6Tk6eTpGxU1fL8L7nSMt6FSIRYr45z+/NXGfOp3/DxKpN8fLJ95tkSO/G0qJe+YjGCCDzXK4oqVutVKTDAAAgd5Pmxx9/XKKiosTr9Zoxy5MnTw563N69e9Ndp7cjaUZ+oz2cmsidqbJQr9fjXh1eVmJjWJc0q2iYAADkR3OXbpfvlu3I8u16tK0hPdvVDEtMAHKYND///PPZuRmQby1bvz9Nz2dG9Ljl6w9Ih6aVwx5XfkLDBJA/aQP8qcTU98+4wjGmcR0oaPQ1cDghMVu3A2DTpPnKK6/M/UgAB1u1MV70e15mJrHR41ZuJGnOKhomgPzJ7fH6etj6dKwtMdEkzSh4tMGoTInC6RqUjhxPMtulixcK2qCktwMQfrzSgFxw/HRypmd91eP0eGQNDRNA/hUdna15SYF8Q0usA8usE5PcMmz0ArP9wq0dpFAs1VOA45LmxYsXy5dffmmWlUpMDF1Ooq1i33zzTXb/DOAIxYvEZimh0+ORNTRMAPlTTLRLLu1YO9JhAACQu0nzDz/8IMOHDzdlI2fC2CQUBC3rl5cVGw9k6lh92bSqzyRVWUXDBAAAAByTNL/xxhsmYdY1mHv27CklSpQgOUaB1rZhRbPcUWbG3BYtEiNtGpI0ZxUNEwAAAHBM0rxlyxZp06aN3HXXXbkfEeBAOkuzrg+syx1l1BGqTUt6HLM6Zx0NE0D+5PZ4ZPXfB812s7rlJNrF+GYAgL1k65OpTJkyUqRIkdyPBnCwFvXKm/WBNWHzZxVh6P4RfZuZ45D9hokz1bTQMAE4i1aGbNubYC6ZnbcAAADbJ8265NSKFStkx46sL8IO5Gct61WQV4efJwMvbujb1+Ls8jK0T2Ozn4Q5Z2iYAPIfV1SUNKpVxlx0GwCAfFGePWzYMFm3bp1cf/31MnjwYGnQoIGUKlUq5PFNmjTJSYyAo2gPZ/vGlWTinPXm91sva8IyEbneMFFWFq/Z5zvH2jDRpkFFU5JNDzPgLC5XlDSoWSbSYSADc5du962lnRU92tZIt4wSABSYpNntdktcXJzEx8fLSy+9lOGxOkHY2rVrsxsfAKRDwwSAjCSnuGXJ2n2+3yd885e0blDBzI1Aw1rWnUpMkcMJidm6HQAU2KRZE+VZs2aZhLhu3bpmjDMAAEBW6WocSckes10o1pXj1ThWbTog781cl2bSwN83x8uqTfFmMkGd84AhHFkTVzhGypQonO7/duR4ktkuXbxQ0P+b3g5A/je3AFSjZOvdbO7cuWaZqY8//ljq1auX+1EBAIACwe3xyuwl28x2n461JSY6KkcJ87ipf6ZbxcCaYEwTaV3lQOdG0KEeyBz9Uhv4xTYxyS3DRi8w2y/c2oFqH6AAO1UAqlGylTQfP35czjvvPBJmAABgm5Js7WE+0wTcer0ep3MjUKoNADkXVwCqUbIVacOGDWXPnj25Hw0AAChQYqJdcsX5Z+X4fpat35+pddyVHrd8/QHp0LRyjv8uABR0PQtANUq2lpy6/fbbzezZEydOzP2IAAAAsmjVxnjf8nNnoset3Hgg3CEBAPKJbPU0Hzp0yJRnv/jii2Zc8znnnCMlS5aUmJjgd/fII4/kNE4AAICQjp9O9o1dPhM9To8HACBsSfOoUaNMXbrWqu/YscNcQtHjSJoBAEAwbo9H1m49bLYb1y4j0a5sFcFJ8SKxpgc5M4mzHqfHAwAQtqT5+eefz87NAAAA0tAk9+9dR812o1rZX8KyZf3ysiKTJdf6N1vVZ/ZsAEAYk+Yrr7wyOzcDAABIwxUVJfVrlPZtZ1fbhhXNOsyZmQysaJEYadOQpBkAkDnZq4EK8Pfff8vixYtl7dq1vjHPAAAAZ+JyRUnj2mXNRbezS5ePGtK7sZzpHvR6PY7lpgAAYU+aPR6PTJgwQTp16iR9+vSRQYMG+WbT/u9//ys33XST7Nq1K7t3DwAAkCUt6pWX4X3PMT3J/qwObN0/om8zcxwAAGEtz05JSZFhw4bJr7/+KrGxsWb27NWrV/uuP3bsmGzYsEH69+8v06ZNk3LlymXnzwAAgHxOJxV1e1Jn74p2RZkJRHOiZb0K8urwsrJ4zT6ZOGe92dfi7PLSpkFFU5JNDzMAIE96midPniy//PKLdOnSRX788Uf5/PPP01yvv/ft21f27dsn77zzTnb+BAAAKAA0Yf520VZzsZLnnNLEuH3jSr7fb72siXRoWpmEGQCQd0mz1Xv82muvSfny6UucChcuLE899ZRUqVJFfv755+xFBgAAAACAE8uzt23bZnqZixQpEvKY6Ohoadq0qemRBgAACEZLsvt0rO3bBgAgX/Q0a7IcHx9/xuP2799vep3Dbc+ePXLfffeZSclatmxpxlIvWrQo2xOcXXPNNXLnnXfmepwAACAtHcMcE+0yl5yOZwYAwDZJc/Pmzc3EX+vXp06wEcyff/4pa9asMceGkybvmiTPmTNHOnfubBJe7QnX2bx/+OGHLN/fM888k2ZSMwAAAABAwZWtpHno0KHidrtlyJAh8tVXX8nu3bt91x05ckS++eYbM7u29toOHDhQwun11183f/+NN96Q5557Th566CEz5lrHWuu46qSkpEzdz+nTp01v9ccffxzWeAEAwL88Hq+s3XrIXHQbAIB8kTS3bdtWHn/8cZMgP/jgg3LhhReakqpvv/1WOnToIA888IAcPHhQ7r77bunYsaOEy4kTJ0zSrr3Z2stsqVSpklkneu/evbJw4cIz3o+Wcvfu3dsk+1riDQAA8obH65WNO46Yi24DAJAvJgJT1113nbRu3Vo++ugjWbp0qVleSnufK1SoYPbfeOONYS/N1jJq7Ulu3759uuusfRpb165dM7wfTZY1Adeear2dNgIAAIDw02HMdauV8m0DAJBvkmZVr149efrppyVStm/fbn7WrFkz3XXVqlUzP7du3XrG+7n66qvlkUcekeLFi8vOnTvDECkAAAgm2uWSc84qF+kwAAAIT9IcaVoerkqUKJHuOmvf8ePHz3g/bdq0kXCyY8u5f0y6bccYA1kxOilWa9sJMSvOcXhxfsPLSefXqTE7KV6ew+HF+Q0/znF4cX7DK8qB59dxSbOOT9Zy74wMHz7ctzRFoUKF0l1v7UtMTJRI0hBjY6PFbtx+w8ZiYqNtGWOwc6nrf+tPuw97c+L5VZzj8OL8hpeTzq9TY3ZSvDyHw4vzG36c4/Di/IaX2wHnNyuJvC2T5h49evh6kUNp0KCBrzw7OTk53fXWrNlxcXESSfqETk52i92k+MWk29maES6PWW8QKSlu279ROPH8Ks5xeHF+w8tJ59epMYcj3hS3R75dlDqUqk/H2ma95ly5X57DYcX5DT/OcXhxfsMrxQHnNyvn0JZJs44vzowvvvjC/ExISEh3nbUvWOl2XrPjk9o/Jt22Y4yhOCFeJ59fp8Ts5HNs53jnzZsjs2bNkBdeHOPbp7Fed91V8tBDj8s554R3gsf8fn7zS8y5Ga//feX2/Qb7G07ghHg5v+HHOQ4vzm94eR18fh2TNGdW7dq1zc9gk3dZ++rUqZPncQGA02zfvs0kzFWrVpOSJUvJr7/MlyPblognJVEWL442+7SCZ8KEN6Rfv+ulTJmykQ4Z+US0K0ouaV/Ltw0AgN04Omlu0qSJFClSRJYtW5buOl1qSrVo0SICkQGAs5w+fUq2bt0i06d/KQkJx+TAgQPidVcXrztJJk96X9avXyvPPvuEtGjRylTykDQjt+j8JIUL2W+sGwAAFjuWl2da0aJFpXv37rJ8+XKZP3++b79OIjZp0iSpUqWKmVQMAJCx+vUbyogRd5t5IM46q6507dpdypx1vpSr313atj1XGjRoZN5z77rrXqlZM7VXEAAAoCDIdk/zjh075PPPP5fNmzfLqVOnxOPxhGxB/vDDDyVcRo4cKQsXLjSzaffp00dKliwpM2fOlMOHD8v48eMlNjbWd+ySJUtMD3S7du2kffv2YYsJAJxo6tTPpVOnLtKr16XywAMjpUTLYeJ1p8js2TNkwoQPZOzYV+Xbb7+W/v0HRDpU5CMej1c27Uyd/LNe9dLiokQbAJAfkub169fLDTfcICdPnhTvGUZ1W8tChUvVqlVlypQp8vLLL8sPP/wgbrdbGjVqZH7v0KFDmmM1YR43bpxJsEmaASCtYcNGmNUItLHxw4++kP+OWywSKzJp8lQpXixOHn30qaBL/AE54fF6Zd22w2a7brVS4hKSZgBAPkiaX3/9dTlx4oScf/75cu2110qFChXMmmGRUrNmTRkz5t+ZXkMZMWKEuWSkevXqsmHDhlyMDgCcw6rO8U+OrW0SZoSDtq3Xqpy60kWY29kBAMi7pFnHEGuiOmHCBHG5HD0sGgAARFC0yyUt61WIdBgAAISUrYxXS6AbNmxIwgwAAAAAyNeylfU2btxYNm7cmPvRAAAAAADg9KT5jjvukG3btsnbb7+d+xEBAIACI8XtkRmLtpqLbgMAkC/GNO/atUsuuOACefXVV2XatGnSrFkzKVGiRMiZsh955JGcxgkAAPIpN8kyACC/Jc2aBGuCrMtNbd261VxC0eNImgEAQDDRrijp0baGbxso6ObNmyOzZs2QF15MuzLM9ddfJQ899Licc07ziMWWH3B+kWdJ8/PPP5+tPwYAABDYuF60SOpSZ0BBtn37NpPQVa1aTUqWLCW//jJfjmxbIp6URFm8ONrsS0pKkgkT3pB+/a6XMmXKRjpkR+H8Is+T5iuvvDJHfxQAAADO6pl79dU30uzf8tNLUrl5PxHpkqnjkbHTp0/J1q1bZPr0LyUh4ZgcOHBAvO7q4nUnyeRJ78v69Wvl2WefkBYtWklCQgJJXRZxfpHnSbO/HTt2yIoVK8wTr1ChQlKuXDlp06aNVK5cOad3DQAA8jmPxytb9hwz23WqlBQXJdq27ZlbsOAnOXLksJw8eVKqVK0h0bFFxetJkXffGS/XX3+DSTSCHX/55VdF+qE4Qv36DWXEiLvljjuGSoUKFaRr1+4y++/y5rq2ZTeYYZGa+N11173m/CJrOL+ISNJ8+PBheeyxx+T77783v+sTzX8isK5du8rTTz9tkmgAAIBgPF6v/PnPQbNdq3IJcQlJs1175g4ejJfOnS8w+z+e/L6cPrJD9v4+RfYX72AS5lDHI/OmTv1cOnXqIr16XSoPPDBSSrQcJl53isyePUMmTPhAxo59Vb799mvp339ApEN1JM4v8jRpPnHihNx0002yefNmKV++vHTr1k2qVasmHo9Hdu7cKQsWLJAff/zRbH/++edSpEiRbAcIAADyL21vr16huG8b9u2Zu/DCHnLllVeb65KSU+Sf3UdNaevwESOlfLnUUtZQxyNzhg0bIcnJyRIbGysffvSF/HfcYpFYkUmTp0rxYnHy6KNPmcpOZA/nF3maNL/33nsmYe7Tp488++yzUrhw4TTX6yD6hx9+WL799lv56KOP5JZbbsl2gAAAIP+KdrmkTcOKkQ4DmeiZGzVqpHTvfrEkJp6WObNnSNU2Q+TA2m9l1qxvZMBNA0MeX7x4aqMIMkcTOuWfvFnbJHQ5x/lFniXNs2fPlooVK8pzzz0X9Mml+zSZXrJkicyYMYOkGQAAwOE9c59+Os18x9Mk+KNJU00vXeUW18l1112Y4fEA4HSu7Nxo165d0rJlywzfCPU6PUZLtAEAzpGc4pYla/f5fp/wzV+yaM0esx9AwZNRz5wrOiZTxwNAgetpjouLMxOBnYkewxsmADjHqk0H5L2Z6+Tk6RTfvt83x8uqTfHyyfebZEjvxtKiXupso0BuSHF7ZN6yHWa7e9saEhOdrfZ8AADCJlufTM2bN5eVK1fK6tWrQx6j1+lSVM2aNctJfACAPEyYx039M03CrLze1J+6f+zU1eY4IDclJrvNBQCAfJM0Dxo0SNxutwwdOlQ+/vhjs0azZf/+/WafXqezaeuxAAB709Jr7WH+//w4JL1ej6NUG7kl2hUl3VpVNxfdBgAgX5Rnn3vuufLAAw/Iiy++KM8884y5xMSk3lVKSmoPha7ZfN9990mHDh1yN2IAQK5btn5/uh7mUPS45esPSIemlcMeF/I//b5QshhDuQAA+SxpVgMHDpQ2bdrIpEmTTBm29jZ7vV6zXnPr1q3lhhtuoDQbABxi1cZ4s0auVYqdET1u5UaSZgAAUDBkO2lWTZs2Nb3NAABnO346OVMJs9Lj9HggN3g8Xtm+P8Fs16xYQlyUaAMA8lPSDADIH4oXic1ST7MeD+QGj9crv2+KN9vVKxQXl5A0AwDshXUdAADSsn75LPU0t6pfIdwhoYDQRpgq5YqZi24DAGA3JM0AAGnbsKIULZK54iM9rk1DkmbkjmiXS9o3rmQuug0AgN3w6QQAkNiYaBnSu/EZC2P1ej1OjwcAACgIGNMMADBa1Csvw/ueY9Zh9l9+yhrrrD3MmjDrcYCdzF26Xb5btiPNPl3RwzJqwmKztFWgHm1rSM92NfMkRiArklPcsmTtPt/vE775S1o3qGCqgmi0BPJh0pyQkCAlSpQI958BAOSClvUqyKvDy8riNftk4pz1Zl+Ls8tLmwYVTUk2X9aQ21LcHvlx5S6z3a1VNYmJznoR3KnEFDmckBjy+iPHk0LeDrCbVZsOpGu8/H1zvKzaFC+ffL+JxstcQKME8qQ8+8ILL8zUUlP333+/XHLJJdn5E8jjN4pFa/aY/QCgXxh0fKnl1suamDWZ+SKBcDl5OtlcsiuucIyUKVE4yxe9HbKO7xHhTZjHTf0zTcKsrMIJ3T926mpzHLJHz93d4xb6GoatRol3v11n9luz+QP+svVpsWvXLjl48GCGx6SkpMjff/8tx44dy86fQJjQegkAsBNdl7lLi2q+7ezQEutgZdZakR0bGy3Jye5Mzw6PjPE9Iny00UHP7Zmeqnq9HqdVQTRmZq9RIvAcBzZK6FAlrbwCspQ033jjjbJ27do0+2bNmiXff/99yNskJSWJ2+2Whg0bZuZPIA/wRgEAsBtXVJTp9YX98T0ivJat35+uhzkUPW75+gOmCgiZQ6MEwl6efd9998nJkyd9F51MQ3uS/fcFXqKjo6VevXry5JNP5ihAROaNghIrAABg4XtE+K3aGJ/ptcr1uJUbKdEOd6MEMmfevDly9913pNu/5aeX5NShrZk+3vE9zc2bN5f16/+t+9fe48suu0xeeumlcMaGXETrJQDAjjxer+w6cMJsV6tQzPQ8w374HhF+x08nZ3oYgR6nxyPrjRKZOcdWowTP4Yxt377NJMBVq1aTkiVLyYIFP8mRI4dNB2qVqjUkOraoeD0p8u474+X6628wE0QHO/7yy6+SfDmm+fnnn5caNWrkfjQIG94oAAB25PF4ZcWG/Wa7Srna4oomabYjvkeEX/EisVk6x3o8Mo9Gidx3+vQp2bp1i0yf/qUkJByTgwfjpXPnC8z+jye/L6eP7JC9v0+R/cU7mIQ51PH5dvbsK6+80vQ+f/TRRzJ69Og01y1YsED69etnroN98EYBALCrCqXjzAX2xfeI8GtZv3yWznGr+owbz06jRGbQKJE59es3lBEj7pa4uDg566y6cuGFPaRfv/5y881DpU3bc6VwqWriiiksw0eMlJo1a4U8Pt8mzdrl/p///Mf0OM+dOzfNdfv375fVq1eb6wYPHmwmBEPk8UYBALAjXZf5vHOqmEt21mhG3uB7RPjpGsFFi2SuCFSPa9OQpDkraJQIj6lTP5dOnbrIQw89IZMnT5Tjx4+bHuQ5s2dI1TYDpFCJyjJr1jcZHu8E2fp0ev/992XlypVy3nnnydixY9Ncd80115hZtbt06SKLFi2Sd955J7diRQ7wRgEAALKL7xHhpzM165JdZ2qb0Ov1OGZ2zhoaJcJj2LARcvvtd0q9evXl00+nSfHixaVcufLy0aSpEhtXWiq3uE6uu+6mDI/Pt0nznDk6gLuqvPnmm1K/fv1011evXl3GjBkjFStWlG+++bdlAZHDGwUAAMguvkfkDV3jWpfsCjzXVi+/7h/RtxlrYWcDjRLhExubWllSqFAh3z5r2xUdk6nj82XSvHPnTmnatKnvAQejJ6FZs2aye/funMSHXMIbBQDAjlLcHvlhxU5z0W3YE98j8o6ucf3q8PNk4MUNfftanF1ehvZpbPaTMGcfjRLI06S5VKlSsmfPnjMeFx8f75gu94KANwoAgB0lnEwyF9gb3yPyjjY6tG9cyff7rZc1MbOR0xiRczRKIM+WnGrfvr3MmDHDTALWs2fPoMfoLNqrVq2SHj16SLhpAq+zeC9evFhOnDghjRo1kuHDh0vHjh0zdfvExER56623ZNasWbJr1y4pWrSotG7dWkaMGCGNGzeW/PdGUVYWr9knE+es971RtGlQ0ZRS8WYMAMhLLleUdDqnim8b9sb3COSnRgnrOayNEoViee4il5PmoUOHyrx582TkyJFy2WWXSefOnaVKldQPvH379smvv/4q06dPNyXaw4YNk3DS3uz+/fubn5deeqnp2Z45c6YMGjRI3njjDbnwwgszvH1KSoo5dvny5abk/MYbbzQzgH/33XeycOFCmThxorRq1UryE94oAAB24YqKkvIsN+UofI8AUNBkK2muV6+evPbaa/LQQw+Z5Pirr75Kc73X65WSJUvKCy+8YHp9w+n1118346Z1lm5N3pUuddW3b1956qmn5Pzzz89wkLnGrwmzHv/ss89K1P/XGC1btswsq6X3Efj4AAAAAAAFQ7aSZqVLSml5ti4vpUnngQMHxO12S/ny5aVly5bSq1cvKVGihISTlmJrQtu8eXNfwqwqVaokN910kynZ1t7irl27hrwP7VFWd955py9hVm3btpV27dqZkm/txdbHBQAAcpfH65V9h06a7Upli5qeZwAA8kXSrLQU+oorrjCXSFi9erUkJSWZMdaBrH1Lly7NMGm+/PLLzSzflStXTndd4cKFzc+TJ1M/zAEAQO7yeLyyZO0+s92nY21xRZM0AwDyUdKsEhISzIRfOpZZ124+77zzZPPmzVK3bt00PbfhsH37dvOzZs2a6a6rVq2a+bl169YM76NPnz5B9x85csT0oBcrVsw3XhsAAOS+siWLRDoEAAByP2k+fvy4vPTSS6Y8Ojk52ezTibg0aX7kkUfk4MGDZtxzkyZNJFw0sVXBysCtfRpndrz88svmtlrmndF61Jlhx0oz/5h0244xBrJidFKs1rYTYlac4/Di/IaXk86vU2MOR7yxMS7p0qKqhAPnN3x4jwg/znF4cX7DK8qB5zfXk+ZTp07JgAEDZO3atWasr47/nT17tu96nXhrx44dZiItnWirRo0aWbp/HZ+sPdcZ0SWlrJ7sYBN9Wft0Oamsevvtt+WLL74wvdU61jknNMRYG84o6fb+ux0TG23LGIOdy+joaPPT6xe/HTnx/Nr5HM9avE1m/7Yt5PWjJvwWdP8l59aSXh1qiV3Y9fzml+ewk86vU2Mm3vByUry8R4Qf5zi8OL/h5XbA+c1KIp+tpFlnqtaEuV+/fqZXWRNU/6T5o48+krFjx5olnyZMmCDPPPNMlu5f13a2epFDadCgga882+rp9qdjnVVcXNaWsRg/fryZkbt06dJm7WadBTwn9AmdnOwWu0nxi0m3XWJ/1htESorb9m8UTjy/dj7HJ04lyeGE0A1goa7T29np9WfX85tfnsNOOr9OjZl4w8tJ8fIeEX6c4/Di/IZXigPOb1bOYbaSZl0HWccvP/7446a1I5gRI0aY43QirqzSRDwztDfYGlcdyNqX2Rm8PR6PPP300/LJJ5+YhPn999+X+vXrS26w45PaPybdtmOMoTghXiefXzvGXKRQjJQpUThbt7PT47Dr+c1vz2GnxevEmHMz3hS3R379c4/Z7nROFYmJzv2vVgX5/IYL7xHhxzkOL85veHkdfH5zLWnWdZG7desWMmG2aNI5f/58CZfatWubnzt37kx3nbWvTp06Z7yflJQUue+++2TWrFlmFm1NmHUiMwD20LNdTXMJNfxBe5Od/mYMFGRHMqgkAQDAkUmz9t5q4nwmmriGc61mnWSsSJEismzZsnTXWT3cLVq0OOP9PProoyZh1iT8gw8+ML3oAAAg/FyuKDm3SWXfNgDA2ZJT3L6lBNWEb/6S1g0qSNuGFSU2xn5jmzMjWzVQOvHXmjVrZPHixSGP+eWXX8y45zZt2ki4FC1aVLp3726WhvLv0dZJxCZNmmSWitJJxTLy9ddfy7Rp00wPs96GhBkAgLzjioqSymWLmotuAwCca9WmA3L3uIUycc56377fN8fLu9+uM/t/3xQvBaan+fbbb5effvpJhg0bJkOGDJH27dub/adPn5a//vpLfv75ZzMDdUxMjLk+nEaOHCkLFy40s2nrmss6cZeOpT58+LCZ1Mt/uaglS5aYHuh27dr5Yh4zZoxvYrEpU6YE/Rv9+/eXcuXKhfVxAAAAAICTE+ZxU/+UwBFz1hC6k6dTZOzU1TK87znSsl4FyfdJsyaYOsO0jgPWGbL1oss/zZs3z1y8Xq8ULlxYnn/+eTnnnHMknLRnWJNdXVf5hx9+ELfbLY0aNTK/d+jQIc2xmjCPGzfOJNiaNO/du9c39nnBggXmEkzPnj1JmgEACAOP1yvxR06Z7fKl4+htBgCHlmS/N3NduoQ5kF6vx706vKyjSrWzlTSrrl27yty5c80M1jqmWBNQTVgrVKggrVu3NstR5VWpc82aNX09xhnRGb31YtGS7A0bNoQ5OgAAEIrH45VFa/aa7T4da4srmqQZAJxm2fr9pic5M/S45esPSIemqfNZ5Juk+ZJLLpHzzjvPtxSUJsna83rWWWfJbbfdZi4AAADZUap41peUAwDYx6qN8b51pM9Ej1u50VlJc6YmAtu1a5fEx/87aPumm26St956K5xxAQCAAkDXZe7aspq5hGONZgBA+B0/nZzp5T/1OD3eSTLV0xwXFyerVq2S9evXS6VKlcy+pKQkOXLkSKb+SOnSpXMWJQAAAADAlooXic1ST7Men++S5k6dOpkZqa+88krzu076peOZ9XImeqwuPQUAAAAAyH9a1i8vKzYeyNSxmli3qp8PZ89+9NFHJTo6WlavXm16mPfs2SNFihSRMmXKhD9CAACQb6W4PfLbX/vM9rlNKlGiDQAO1LZhRfnk+02ZmgysaJEYadMwHybN+/btk8cee0yKFy9ufm/YsKF0795dXnrppXDHBwAA8rn4o6lLTgEAnCk2JlqG9G5s1mHOqEJb10fQ45y03JTKVHPu4MGDZdSoUb7fdZ3jHj16hDMuAABQALhcUaaHQi+6DQBwphb1ysvwvueYnuTAMcxK94/o28wc5zSZ6mkOnPBr3Lhxcvnll8tFF10UrrgAAEAB4IqKkmoVUivZAADO1rJeBXl1eFlZvGafTJyz3uxrcXZ5adOgoinJdloPc5aS5vLly8vChQvlo48+ksqVU9fT2r17t3z33XeZ+iP0SgMAAABA/hcbEy3tG1fyJc23XtZECsU6M1nOUtKss2a/+eab8vzzz/tmxF6+fLm5ZMa6detyFiUAAMiXPF6vHD6WaLbLlCxsep4BAHBc0nznnXdK9erV5Y8//pDk5GSZPn261KxZU1q3bh3+CAEAQL7l8Xjll9W7zXafjrXFFU3SDABwYNKsPct9+/Y1F6VJc4sWLXw9zwAAANlVLC420iEAAJCzpDmQjm0uV65cdm4KAADgo+syd29TI9JhAACQu0lzu3btMnXc2rVrZc+ePXLhhRdm588AAAAAAGD/dZo1SX7iiSeCXvfVV1/JihUrgl73wQcfmDWdAQAAAADIt0nzsWPH5OTJk0GvGzVqlEyZMiW34wIAAAWA2+ORxX/tNRfdBgAgX5RnAwAA5AavV2TfoZO+bQAA7IakGQAARIyuy9yqfgXfNgAAdkPSDAAAIsblipKalUpEOgwAAHI2phkAAAAAgIKInmYAABAxHq9XEk4kme0SxQpRog0AsB16mgEAQMR4PF75adUuc9FtAADshp5mAAAQUUUK8XUEAGBfmf6UWrRokQwYMCBL1/399985iw4AAORrMdEuubh9zUiHAQBAzpPm+Ph4c8nqdVGMTQIAAAAA5Oek+aOPPgp/JAAAAAAAODFpbteuXfgjAQAABY7b45EVGw6Y7dYNKki0izlKAQD2wicTAACIGK9XZHf8CXPRbQAA7IbpKgEAQMTouszN6pb3bQMAYDckzQAAIGJcrig5q2rJSIcBAEBIlGcDAAAAABACPc0AACBivF6vnDidYraLFYlhqUoAgO3Q0wwAACLG7fHK98t3mItuAwBgN/Q0AwCAiIqNoQ0fAGBfJM0AACBiYqJd0rtD7UiHAQBASCTNAFDAzV26Xb5btiPdOFPLqAmLg44z7dG2hvRsVzNPYgQAAIgUkmYAKOBOJabI4YTEkNcfOZ4U8nYAAAD5HUkzABRwcYVjpEyJwtm6HZBTbo9H/th80Gw3P7ucRLsY3wwAsJd88Y1nz549Mnr0aFm8eLGcOHFCGjVqJMOHD5eOHTtm6vanTp2Sd999V2bOnCm7d++WihUrSu/eveW2226TuLi4sMcPAJGkJdbByqy1Ijs2NlqSk93iV60N5Cp9bm3fl2C2m9UtF+lwAABIx/HNufHx8dK/f3+ZM2eOdO7cWa655hrZtm2bDBo0SH744Ycz3t7tdsuwYcNk3LhxUr58ebnhhhukVq1a8tZbb8mAAQMkKSl4WSIAAMg5V1SUNKlT1lx0GwAAu3F8T/Prr79ueoffeecdkzSrwYMHS9++feWpp56S888/XwoVKhTy9jNmzDA91Jp4P/744779r7zyirz99tsybdo0ue666/LksQAAUNC4XFFSr3rpSIcBAED+TJq1FPurr76S5s2b+xJmValSJbnppptMyfbChQula9euIe9De6XLlCkjt9xyS5r9Wp6tSfPvv/9O0gwAAAosZtgHUNA5OmlevXq1KZ9u3759uuusfUuXLs0waf7vf/9rLoG2bNlifpYrx/gqAADCRZOv00lus12kUHTQ5AuRxQz7AAo6RyfN27dvNz9r1kzfilmtWjXzc+vWrVm6z6NHj8qvv/4qTz/9tJQsWZJeZgAAwsjt8ZqeTNWnY22JiSZpthtm2AdQ0Dn63ezIkSPmZ4kSJdJdZ+07fvx4pu9v+vTpMmrUKLOts2brjNo1atTIcZx2bDT3j0m37RhjICtGJ8VqbTshZqedY0W84UW84ee0mMMRr96Xjmu2tnP7vv1/2p1d4724fU1zCaRxxsRES0qKM2bYt+v5zU/fJZx0jjm/4RXlwPPruKRZxyfv27cvw2N0SSmrhCvYRF/WvsTE0OVEgUqXLi1Dhw6VvXv3yty5c2XIkCEyduxYM5lYdllLttiN2+/DLSY22pYxBjuX0dFaupe6RImdOfH8Ou0cK+INL+INP6fFHI549f3x6q5nSzhwfsOLeMPLid8lnHSOOb/h5XbA+c1KIm/LpLlHjx6+XuRQGjRo4CvPTk5OTne9tVRUVtZZ1rHP1vhnnYFbS7Pvv/9+s3RV0aJFJTv0Ca1rnNpNil9Muu2EtcesNwgntGg78fw67Rwr4g0v4g0/p8VMvOFFvOHltHid+F3CSeeY8xteKQ44v1k5h7ZMmh955JFMHffFF1+YnwkJCemus/YFK93OjEaNGskVV1whn332mZlBu2PHjpJddnxS+8ek23aMMRQnxOvk8+vEmIk3vIg3/JwWM/GGF/EWvHjPNEP5A285a4ZyO57j/PRdzQnxeh18fh2TNGdW7dq1zc+dO3emu87aV6dOnQzvY/ny5XL48GHp3r17yMnE9HoAAJD73B6PrPnnkNluelZZiXbZsT8CCC9mKAfszdFJc5MmTaRIkSKybNmydNfpUlOqRYsWGd7Hk08+KZs3b5ZffvlFypcvn+a69evXm5+5MRkYAABIT3sftuw5Zrab1Ckb6XCAiGCGcsDeHP1K03HG2kM8Y8YMmT9/vlxwwQVmv04iNmnSJKlSpYqZVCwjvXv3lldffVVGjx4tzz33nG+/JtGzZ8+W+vXrS9OmTcP+WAAAKIhcUVHSsGYZ3zZQEGmJdbAya2tCWZ0fx+nlrYCTOTppViNHjpSFCxea2bT79Olj1laeOXOmKakeP368xMbG+o5dsmSJ6YFu166dtG/f3uwbOHCgmehr6tSppse5devWsmPHDrNP7+t///ufuCgVAwAgLHS5qYa1UpNmAADsyPHZYNWqVWXKlCnSrVs3k+h++eWXZqzze++95+t5tmjCPG7cOF/pttLy7o8++khuueUWiY+PN9urVq0yk4BpIt2wYcMIPCoAAAAAgB04vqdZ1axZU8aMGXPG40aMGGEugXRZqnvuucdcAABA3tEZglPcHrMdE+0KOkMwAACR5PieZgAA4Fxuj1dmLt5mLroNAIDdkDQDAAAAAJCfy7OBSJq7dLt8t2xHunJDy6gJi4OWG/ZoWyPoTJkAUJBEu6LksvPqmG0qswEAdkTSDOTQqcQUOZyQGPL6I8eTQt4OAAo6bVQkWQYA2BlJM5BDcYVjpEyJwtm6HQAAAAB741s7kENaYh2szFp7TmJjoyU52S1+1doAAD8ej1fWbjtkthvXKmvWbQYAwE6YCAwAAESMx+uVzTuPmotuAwBgN/Q0AwCAiHFFRcnZ1Uv5tgEAsBuSZgAAEDFajt20TrlIhwEAQEiUZwMAAAAAEAI9zQAAIGJ0XXtrKLNWZwdb1x4AcmLu0u3y3bId6d57LKMmLA763tOjbY2gk72i4CFpBgAAEeP2eOXbRVvNdp+OtSUmmqQZQO46lZgihxMSQ15/5HhSyNsBiqQZAAAAQL4VVzhGypQonK3bAYpnAgAAiJhoV5T07lDLtw0AuU1LrIOVWWtFdmxstCQnu33DRIBgSJoBAEDE6DjC2JjoSIcBAEBIzJ4NAAAAAEAI9DQDAICI8Xi8snHHEbNdv0Zps24zAAB2QtIMAAAixuP1yvrth8322dVLiUtImgEA9kLSDAAAIkYn4qlTpaRvGwAAuyFpBgAAERPtcknzs8tHOgwAAEJiIjAAAAAAAEIgaQYAAAAAIATKswEAQMSkuD0ya/E2s92rQy2JiaY9HwBgLyTNAAAg4jNoAwBgVyTNAAAgYqJdUdKzXU3fNgAAdkPSDAAAIiYqKkriCvN1BABgXwwcAgAAAAAgBJp2AQBAxHg8Xvl791GzXbdqKXFRog0AsBmSZgAAENFJwP7acshs16lSUlxC0gwAsBeSZgAAEDFRUSI1K5XwbQMAYDckzQAAIGKiXS5pVb9CpMMAAGTT3KXb5btlO9Ls8/otJThqwmIz6WOgHm1r+FZPsDuSZgAAAABAtpxKTJHDCYkhrz9yPCnk7ZyCpBkAAAAAkC1xhWOkTInC2bqdUzgnUgAAkO+kuD2mtE9pmV5MNKthAoCT9GxXM2iZtVZkx8ZGS3KyW/yqtR2JpBkAAERUcoon0iEAABASSTMAAIiYaFeUXNSmhm8bAAC7IWkGAAARozOqFo+LjXQYAACExMAhAAAAAADyc9K8Z88eue+++6RTp07SsmVL6d+/vyxatCjb9zd9+nRp0KCBTJ48OVfjBAAAaXk8Xvln9zFz0W0AAOzG8UlzfHy8SZLnzJkjnTt3lmuuuUa2bdsmgwYNkh9++CHL93fw4EF54YUXwhIrAABIy+P1yuq/481FtwEAsBvHj2l+/fXXZffu3fLOO++YpFkNHjxY+vbtK0899ZScf/75UqhQoUzf39NPPy1HjhwJY8QAAMB/SZKq5Yv5tgEAsBtH9zSfOHFCvvrqK2nevLkvYVaVKlWSm266Sfbu3SsLFy7M9P1pz/Ts2bOlS5cuYYoYAAD4i3a5pF2jSuai2wAA2I2jP51Wr14tSUlJ0r59+3TXWfuWLl2aqfs6fvy4PPnkk9K1a1e5+OKLcz1WAAAAAIDzODpp3r59u/lZs2bNdNdVq1bN/Ny6dWum7uull16ShIQEefzxx3M5SgAAAACAUzl6TLM19rhEiRLprrP2aQ/ymWhv9Oeffy4PP/ywVKlSJdfjtOMYLf+YdNuOMQayYnRCrE6M14kxE294EW/4OS3mcMSb4vbI98t3mu2L2lSXmOjca8/n/IYX8Yaf02Im3vAi3sixZdKs45P37duX4THDhw+XqP//DwSb6Mval5iYmOH96PWPPvqoNGvWTG644QbJbRpibGy02I3bb4LSmNhoW8YY7FxGR0ebn06YYNVp8ToxZuINL+INP6fFHI54o1xRkpTiMdv6WZTbSXNBP7/hRLzh57SYiTe8iDd3ZSWZt2XS3KNHjzPOYK3rKFvl2cnJyemu17HOKi4uLsP7GTt2rOzatUvGjBkjrjBMQKJPkORkt9hNil9Muu2EOn3rBZeS4rblC8/p8ToxZuINL+INP6fFHI54dZmpLs1Tq7zcbo94c3GtZs5veBFv+DktZuINL+LNXVmJyZZJ8yOPPJKp47744gvzU8ciB7L2BSvdtqxdu1Y++OADGTJkiEnCw8XuTxLdtmOMoRBv+DktZuINL+INP6fFnJvxRkmUlCxWOM1957aCfH7zAvGGn9NiJt7wIt6854QOxpBq165tfu7cmToWyp+1r06dOhkuMZWSkiJvvfWWSZqty4MPPuhbs1l/nzZtWtgeAwAAAADAvmzZ05xZTZo0kSJFisiyZcvSXWctNdWiRYuQt2/Xrp0ZGx1o3bp1JqHWsdU61rlRo0a5HDkAAFAej1d2HkidtLN6heLicuWDGWMAAPmKo5PmokWLSvfu3WXGjBkyf/58ueCCC8x+nURs0qRJZiZsTXxD0bWcg63xrD3LmjR36dJFbrzxxrA+BgAACjId07xy4wGzXbV8MXEJSTMAwF4cnTSrkSNHysKFC02PcZ8+faRkyZIyc+ZMOXz4sIwfP15iY2N9xy5ZssT0QGsPc7BkGQAA5P1EMZXKFvVtAwBgN44e06yqVq0qU6ZMkW7dupne4S+//NKMdX7vvfd8Pc8WTZjHjRvnK90GAACRFe1ySYcmlc1FtwEAsJsor9fpc5nZmy6fcejQCbGbxCS3DBu9wGy/dU8XKeSQdZp1DU9dwssJz1qnxevEmIk3vIg3/JwWM/GGF/GGl9PidWLMxBtexJu7ypYtJtHRmWuspUkXAAAAAID8OqYZAAA4V4rbIz+t2mW2u7asJjGZbPUHACCvkDQDAICIOnEqOdIhAAAQEkkzAACIGF2X+fxmVX3bAADYDUkzAACIGFdUlJQrVSTSYQAAEBIDhwAAAAAACIGeZgAAEDEer1f2xKcuzVilfDHT8wwAgJ3Q0wwAACLG4/HKsvX7zUW3AQCwG3qaAQBARJUvFRfpEAAACImkGQAARIyuy9ypWZVIhwEAQEiUZwMAAAAAEAJJMwAAAAAAIVCeDQAAIibF7ZFfVu8x2+c3q2LKtQEAsBOSZgAAEFFHjydGOgQAAEIiaQYAABHjckVJx6aVfdsAANgNSTMAAIgYV1SUVCxTNNJhAAAQEgOHAAAAAAAIgZ5mAAAQMR6vV/YfPmW2K5aJMz3PAADYCT3NAAAgYjwer/z2115z0W0AAOyGnmYAABBRpUsUjnQIAACERNIMAAAiRtdlvqBFtUiHAQBASJRnAwAAAAAQAkkzAAAAAAAhUJ4NAAAiJsXtkUVr9prtjk0rm3JtAADshKQZAABE1KFjpyMdAgAAIZE0AwCAiHG5oqR940q+bQAA7IakGQAARIwrKkqqlCsW6TAAAAiJgUMAAAAAAIRATzMAAIgYj9crh46mjmkuW6qI6XkGAMBO6GkGAAAR4/F45dc/95iLbgMAYDf0NAMAgIgqUbRQpEMAACAkkmYAABAxui7zha2rRzoMAABCojwbAAAAAIAQSJoBAAAAAAiB8mwAABAxKW6PLFm7z2y3b1zJlGsDAGAnJM0AACCiDhw5FekQAAAIiaQZAABEjMsVJa0bVPRtAwBgNyTNAAAgYlxRUVKjYvFIhwEAQP5Omvfs2SOjR4+WxYsXy4kTJ6RRo0YyfPhw6dixY6Zu/80338h9990X9LpWrVrJp59+mssRAwAAAACcwPFJc3x8vPTv39/8vPTSS6V48eIyc+ZMGTRokLzxxhty4YUXnvE+NmzYYH4OHjxY4uLi0lxXpUqVsMUOAEBB5/F65ejxJLNdqngh0/MMAICdOD5pfv3112X37t3yzjvvSOfOnX3Jb9++feWpp56S888/XwoVKpThfWzcuNEk2/fff38eRQ0AAJTH45UFv+8y23061hZXNEkzAMBeHL2ug5Zif/XVV9K8eXNfwqwqVaokN910k+zdu1cWLlyYqZ7mevXqhTlaAAAQTNEiseYCAIAdOTppXr16tSQlJUn79u3TXWftW7p0aYb3ceTIEdm3b5/Ur18/bHECAIDgdF3mHm1rmAtrNAMA7MjRn07bt283P2vWrJnuumrVqpmfW7duPWNptvJ4PDJixAgzeVjLli1Nibcm5QAAAACAgsvRY5q1l1iVKFEi3XXWvuPHj2dqErAvvvhCzjvvPLniiitkx44d8sMPP5he6gkTJmR6Fu5Q7DiniX9Mum3HGANZMTohVifG68SYiTe8iDf8nBYz8YYX8YaX0+J1YszEG17EGzm2TJp1fLKWTGdEl5SK+v//QLCJvqx9iYmJGd6P2+02vdK33367XH311b79mjAPHDhQHnzwQZk3b94ZJxMLRUOMjY0Wu3F7/92OiY22ZYzBzmV0dLT56fWL366cFq8TYybe8CLe8HNazOGI1+3xyNK1+812u8YVJdqVe0VwnN/wIt7wc1rMxBtexJu7spLM2zJp7tGjh68XOZQGDRr4yrOTk5PTXa9jnVXgElKBNDHWS6B27dqZJax0orEVK1ZIhw4dJDv0CZKc7Ba7SfGLSbedUKdvveBSUty2fOE5PV4nxky84UW84ee0mMMRb4rbIzv3J5jtFmeXk5jo3DsRnN/wIt7wc1rMxBtexJu7shKTLZPmRx55JFPHaUm1SkhI/bD1Z+0LVrqdWY0bNzZJ886dOyUn7P4k0W07xhgK8Yaf02Im3vAi3vBzWsy5GW+UREnzs8v7tsNxHgry+c0LxBt+TouZeMOLePOeLZPmzKpdu7b5GSyptfbVqVMnw/tYv369HD16NOgM3FZpd3ZLswEAQMZcriipXblkpMMAACAkJ1TlhtSkSRMpUqSILFu2LN111lJTLVq0yPA+Ro4cKQMGDJD9+1PHU/nTsmzVtGnTXIsZAAAAAOAcjk6aixYtKt27d5fly5fL/Pnzfft1ErFJkyZJlSpVzKRiGenZs6f5+eqrr4rXr25gzpw55j7PPfdcqVu3bhgfBQAABZd+9h47kWQu/p/DAADYhaPLs62e4oULF5rZtPv06SMlS5aUmTNnyuHDh2X8+PESGxvrO3bJkiWmB1on+bLKsXU9Zl1eatq0abJ582Zp06aNbNmyxSTMFStWlGeffTaCjw4AgPzN7fHKjytTh1T16VhbYqLzwdokAIB8xdE9zapq1aoyZcoU6datm0l+v/zySzPW+b333pMLLrggzbGaMI8bN85Xuq2KFy8un3zyiQwaNEgOHjxoeqjXrFljlp+aOnWqVK9ePQKPCgCAgqNwbLS5AABgR1FeaqHCyu32yKFDJ8RuEpPcMmz0ArP91j1dpJADvqxYa17rEl5OeNY6LV4nxky84UW84ee0mIk3vIg3vJwWrxNjJt7wIt7cVbZsMYmOdhWM8myc2dyl2+W7ZTvS7PNvKxk1YbFEBVndu0fbGtKzXc08iREAAAAA7IikuQA4lZgihxNSl88K5sjxpJC3AwAAAICCjKS5AIgrHCNlShTO1u0AAAgnt8cjqzbGm+2W9ctLtMvx060AAPIZsqICQEusg5VZ232cAQAg/9PPn50HjpvtFvXKRzocAADSIWkGAAAR44qKknPOKufbBgDAbkiaAQBAxLhcUVK3WqlIhwEAQEgMHAIAAAAAIAR6mgEAQMToEojWag06AWWwJRABAIgkepoBAEDEuD1e+W7ZDnPRbQAA7IaeZgAAEFHR0bThAwDsi6QZAABETEy0Sy7tWDvSYQAAEBJNuwAAAAAAhEDSDAAAAABACJRnAwCAiHF7PLL674Nmu1ndchLtoj0fAGAvfDIBAICI8XpFtu1NMBfdBgDAbuhpBgAAEeOKipJGtcr4tgEAsBuSZgAAEDEuV5Q0qJmaNAMAYEeUZwMAAAAAEAI9zQAAIGK8Xq8kJXvMdqFYl0RRog0AsBl6mgEAQMS4PV6ZvWSbueg2AAB2E+XVJl6EjZ5ej42/BGiDvpOeAcQbfk6LmXjDi3jDz2kx53a8el8nE5PNdtHCseb+c1NBP7/hRrzh57SYiTe8iDd359TIbHUTSTMAAAAAACFQng0AAAAAQAgkzQAAAAAAhEDSDAAAAABACCTNAAAAAACEQNIMAAAAAEAIJM0AAAAAAIRA0gwAAAAAQAgkzQAAAAAAhEDSDAAAAABACCTNAAAAAACEQNIMAAAAAEAIJM0AAAAAAIRA0gwAAAAAQAgxoa5A/jd58mR5+umnZeXKlVKsWLF01x85ckTGjBkjP/30kxw6dEjq1q0rQ4YMkV69ekmkLVmyRMaPHy+rV6+WQoUKScuWLeX222+XZs2aiR0tXbpUxo0bJ3/++ad4PB5p3Lix3HbbbdKlSxexk2nTpsmDDz6Y4THVqlWTH3/8Uezk2LFj8sYbb8h3330nBw8eNDH26dPHPF8LFy4sduN2u81zNjExMej1+jhq1aoldqXvB71795YqVaqY54wdLViwQCZMmCAbN26U6OhoadeunQwfPlwaNGggdjR//nx57733ZM2aNeb5UadOHbn++uvluuuuE6d/lkRCSkqKiWvKlCmya9cuqVixolx55ZVyyy23SGxsrNjZvn37zOfs3XffLTfeeKPY0Z49e8z3g19++cV8VyhbtqxccMEF8t///lfKlSsndo359ddfl99++00OHz5svtP0799frr76arE7/VzW99oZM2ZI/fr1xY4eeeQR+eKLL4Jep9939PlsN19//bVMmjRJNm3aJCVKlJBWrVqZOPX9104y87n10UcfSfv27cVODh8+LK+++qr5zqjvE5UqVZJLL73UfF/X7+5OQ9JcQC1btkz+97//hbz+5MmTMmjQIFm3bp1ccskl5ok+b94882aiX5gj+UE+c+ZMuffee80X4Z49e0rp0qXl+++/Nx9+r732mlx00UViJ7/++qv5oqZvEJrI6Rc2fQy33nqrvPzyy2afXTRq1MgkFqGSEE367famrAmz/u83b95svrTph502qugXuvXr18vYsWPFbrZu3WoS5vPOO88kz4FKlSoldvbcc8+Z9wFNmu1o+vTpMmrUKKlQoYJcccUV5lx/++23JjHVL0gtWrQQO/nss8/k8ccflzJlypjGCG3o0cZK3afPa/0y6tTPkkh56qmnTMLctm1bufDCC01Cr+8JGzZsMD/t6sSJEzJixAg5fvy42NXu3bvlmmuuMQ2U1nuuflfQ871w4UKTOGkSbSf79+83Mev7Vo8ePcx7l342P/zww+Zzws6vMT2ndm2c9KevLf2/6+dxoDZt2ojdaDL31ltvSe3atU3M2lg1Z84c8/1h6tSpUr16dbGLUN/L9Hn9+eefm886bQSy23tZ//795Z9//pEOHTqY75fagfTmm2+axuF33nlHoqKixFG8KHC+/fZbb/Pmzb3169c3l+PHj6c75s033zTXffrpp759CQkJ3t69e3tbtGjhPXjwoDcSjh075m3durW3SZMm3pUrV/r2Hz582NuzZ09vu3btzLadXHbZZd7GjRt7//zzT9++bdu2eVu2bOnt2LGjNyUlxWt3W7ZsMc+ZXr16eU+dOuW1kyeffDLdc9XtdntvvfVWs3/ZsmVeu5k5c6aJbe7cuV6nmT9/vu+948orr/TajT4/9T2qc+fOad4L1q1bZ943+vXr57WTo0ePmnjPO+887/79+3379X358ssvN+d57dq1Xqd+lkTCihUrTDx33323b5/H4/Hef//9Zr8+h+1o586d5jVlnc9JkyZ57eiee+4x8U2fPj3N/vHjx5v9zz77rNduHnzwQRObvvdakpOTvddff723QYMG3s2bN3vt6OTJk95u3br5nhMbNmyIdEhB6etL38f0c9cJ/vjjD/N/v/HGG72nT5/27Z89e7Y5zw899JDX7vScDxo0yNuwYUPvb7/95rWbDz74wJzL5557Luh3sx9//NHrNIxpLkC0hfWOO+6QkSNHmtbAjMo/P/nkE1PO1q9fP9++4sWLmxIb7YWeNWuWRMLPP/8sCQkJpsXYv4dOe5v1sWn5x1dffSV2kZycbFqxtbSmadOmvv01a9aUc889V+Lj403poBNKw7S37vnnn5ciRYqIXehzUVvgtfTWv4zV5XKZ8p+rrrrKlGnasUVe1atXT5xEW46feOIJ2w0r8Kfl2NrqrqXN+r5gadiwoTnfWi2h5c92ob1d+jzWFnmN26JlzjfffLPZ1hJYp36WRMLHH39sfmqMFu3R0Hj1p/Yi2c3EiRNN2aJ+Xuhng13p8KIffvjB/M+1isPf0KFDTZWEfk7bjfYi6uew//CymJgYufjii7XzSP744w+xI62e0x59/Yyzs+3bt5v3MbuWjgd7j9D3gmeffTbNEC6tXrz22mvNdzS704oO/fy44YYbbFcBqLQ3Wen3MP/vZtZwCLu+5jJC0lyA6JgN/bDTJ7AmllpyHerNTz9gtKxNn+D+rBemllhEws6dO83P5s2bhxzzsWLFCrELLcXWL79aQhOYvB04cMCcX7uX4mq5kpY2akOF3caMa2noqVOnTLldII1Vk3w7fgHVpFkbH+yWbJzJ6NGjTcOUlg3blf7fdUy4NvD500YfLSvVRFqHdtjFWWedZYa9aJlrIOvLnH4ZdeJnSaQsX748aLmixqmlmJH6/DrTeESdi0HHYV9++eViV/o5puOWdchRIH1d6UXfk+1G5wv45ptv0u3fsmWL+WnHcdg6Z4s+L+68807z3LAzqyHYKUmzNuxouXBgcqyJtA7t0OFzdm/A1vH52mh51113iR2V/v9Ga/3c9affh5XdhnBkBklzAaJvDjrpgSYSJUuWDHmcJs2qRo0a6a7TLyL6RU7HZEaCNXGA9uAG0h5oZbeeW2211AT50UcfNY0R1sQI+oGoXzrtnDRrj5wmSprg6Qe3HXsV1dlnn216nPXL5jnnnCPdunUzY5Xs1KMYGHflypXNeGtt2daYde4A/YKkvR52pA0nWoGiX5jt/gXOX1JSkuldHjZsmEn4g33ZjyTtAdcEXycHDKRzNdixIiGznyWR+n/v3bs36OeX0ueuvgcfPXpU7OTJJ580DRA6EZGd6WfwwIEDg06etXjxYtPAo+/HdqbvsfpZrJ8ROp9AkyZNpFOnTmIn+h1Hx1trYvef//xH7M76LNbvj1o1o89jHceqVWpWkmQX2nOv1TL6PNU5I7QqrXXr1uain292+w4ZzAcffGAqFfVzTatA7eiKK64wHUf6ObFq1SrTmKY94zopbvny5W01n09mMRFYAaITX2Rm4h79YqlCfRnSF2ikJimxSpx1UjLt+fRnzehstwlU7rvvPomLizOzfftP5qEfLA899JDYmfYmbdu2zZS66puc3Vgfxh9++KFpOe7evbupkNByVm2Y0A9EnWzNTvT5qS2v+sVNEw+dpEh7QXWSKi0V0/JMnWjLbomIfoHTL5cDBgwQp9BSUh3GYVV53HTTTeYLvxNob6lOGKivO20EcuJnSSSc6fNLZ8i1Glnt1GB5/vnni5OdPn3afDm2GortTD93rc9irTzQWfbtVH2iNKa///5bvvzyS9vFllFPs07ypJVfWg2o5bd6nhctWmQmq7JLRYr1vUFnU9fvkfoc0EYgnbBKK+u0WlFLn+36Hqefx1perhNHBn4PtpOmTZua5F6HxfgPn9OJA3USMCf2NJM0O1znzp1Ni+mZZt3T2Tgzy/qCGWo6eN0faqmccD8OvWivnM7krEucDB482CSkOjOu9oJpq1Ze9NRl5bzrLLgam5Z/aYKk5T+6Tz8MtTRTv8jbKV5/OtOwfmDrec5LmY3XKgPUhPPdd9/19Rbofo1Zl+fQ1sxgpa+Rilnj0Z5D7QmzevGtWcB1DKuOt9Te53CPG87Kc0IbfLQHQWMLHLKRV7LzHNYSNv2w1tecNqro81nfu7T8LtyzdubkvVnLn3U8rib92gOp73FO/CyJhMx8fqnc/gwryLRXVEtEtbdRV68INlzGTnQoly5HqA2U2vOlDdj65d4usyVrY6/2gmsDX7AKFDvS15VWoOjM9No7btEkWsdla0OwlhPbgTXcRYdpaG+oxmY1TOhnxDPPPGMagOw6y75+39Wecn0vzovPhpz06L/66qum0lK/++rzQysstVFCh3hpj3PRokXFSUiaHU4/nKyW9VCyui6pNY4uWAm01cqV20/0zD4O/aKrJa1aYqljv/Si9I1D3/i0NywvJqrKbLw7duzwlbN++umnvpa1e+65x3wg6puz9t6FuyQvO88TLVHSDxVNREOVOkY6Xn0zVvqG7F9ep88H/RKnDRKzZ8/Ok6Q5szFrK6sm84G0Z0yrErQUTz8Uw500ZzZe7UHQBgldgk5LiSMlO89h7VXUYRFWT5iOU9Mej44dO5pyeLvFq/766y/zhV5vq2Od82oJvXB8lkSC9f6f0eeXctqXNbvS15W+12pDsH6Wvfjii2J3/tUmukzWY489ZhrS3n77bYk0bSjT5a90+I7dG6j8vfLKK0H363uudhBo1Zo+V+wwkajV8KuJspaP+/fk66RaWrmmz2dtWPOfJMwudBiHPgY79zJb33M1QX7jjTfSfI7p79ogoQ0T2vnlJCTNDheOtQWtkjVrjHCw8tLcLrPJyuPQkhldh1V7F7V8SRNRTZr0zU1bEPOijDiz8WoPnX550zEz/qUoVoKkH976WMKdNGfneaIfGircyUVO4v3999/NT/2yFshq7bbG6DvhtWg9DmvCu0jHq2PCtdRSX3P+MxFHQk7Pr35Z0zIxXRFAv8CF+3mdnXi1jFF7c7WHXOcQCJzMLJzsvE5tVujwIf1CGerzy9pvlWkj+3RcuCZFOl5RSzF1si27jq8MRd/fdOZyHdKjDSqhKhTyipbd6vl8//33bd2LmFn6WtTGVv1M00oWO0x+ab32tTPDf4UFK16rw0PnRrBDvP60Ik0TUf3OaJdy92D03OkcBzquPbDhV8dha9WaJv9aSRWp6rXsIGlGOjq+I9QXdx0Loq1v2lsWSbpUhL4Q/V+M1oyodlrgXd84lJZhB7Im9wmcWdAutFFCW2DzqqcrJ8/VYL1K1j67ffHQLw46Tlyfp4EztmpLvLJL67aO+dJJtELNWK+9ovoFQ5dD0bI2O9D3LV3qQr9U6LJ5/qpWrWp+6kRQdjN37lzTMq/lxVox46Sx43aiSY/+n0M1POl+ndDSacmd3WiVjw4n0aEE+vrXMly7nlN9X9WVFvSzoE2bNumu1+eLjmfVRgD/Zd8iQWf+V1rZE4wuS6a04c8u5eT6nVBL3fW151+abdfPNa2c0+82oapRrCEedugVD6TDCTQ+HcJlZ3v27An53VeTZN2v1Yxawh3p11xWkDQj6AeIXrQ1S8cH+4/9sxLTFi1aRCzh0AkbevfuLaNGjQr6YWOnCVWspEhnG9ex2P40cVJ2fcPQsSc6u2RgS6yd6GyX6rfffjNl8MHWCLTbEhi67IlOTqa9noHLWljLpfmv6R1JWhGhPZ/B6Hgkfe5qT42dZtPWSQJfeOEFuf/++9ONxdcvdiqvhxtkpodZE2YtzdTyVjsvOeQE+r6gk+xpb5H//1o/P/S9WCcMRPZptZm+tjRh1iovHbMa6R7ajGgFmq4hrZ9nOvQlsJpGxxDr0pA6sVKkXXnllUHXZNaZ9PX9Syfl1O8Vdpq1Xqs3tIJHG7G18c+fzi+ydu1a81mhJed2oMm7fsbqRGX6Pcy/N1kTUj3P+lyw43czq7ouWOOPnZT//4rPUCvt6HuzvmfYaTLGzHBOnzjy1GWXXWZagXQCK/8PSp2cQltrrdbOvKblKNrLrImH//g7TfB0rKL2eumENnahrYHa6KCt8P69W/pBohMkRKr8+Uy0N0Zb3YOVPduJ9tbqh4cuh+T/Zchaw1BbNO2WgOgXdm3l1uWlrDHZVhWHPid0Mjv9AmIH+sVMx9UFuyjtydVtXTrNLnRsrp5DndhHW7Et+vp76aWXzLad4tX3sXvvvdf0eugYL7s9X51IJ/dROtGeNTGk/tTflV1eX06lz1Od60AbqHVsop0TZqVDo3QeA03ytSTUn8avFWH6utPvFpGm703B3m+tHlydtEx/t1PSrAmSfg5rguS/Qoi+5nSss05apcm+nVjvAToXjtWzrLQs3no+2LFseN26debzzW7LEAbSxkr9/qgNwlq1GDgEQZ8r2uBm9/eOQJF/h4AtaausTr2vg/SXLFliyoC0J1dbh5544omItsjqchHa+6UfLvoFWb8M62RPWkqjX4rDPStuVuhYHh2/oWObddZkjVffiPVNRBNTfeMO94RP2aH/Z2XnMTMWfY7q5B06RlxbuXX8rZ5fbUHW53GwcrFI0tZ4HeOuE9pp49PFF19sPrS13E6/XOgYH51lEtmjvd7aa6u9zfqa0/OrvUm6JJ02UuikRc2aNRO70MkMNbnXBggdf6/Pi0C6bJbd1pG1M02QevXqJbNmzTLDX7TnTqs49KJVSnZqWHUa/WywEiP9YqwNwoH0s1jfe+1EJwTUmfS1Qk2rUfQ9VscO60W/3Ot7BrJPJ1PTz2H9fqa94np+ddk8Hd6jrz+7PR/69u1r5m3RWLV3X99fteJAV1nQz+hQFVaRpp8RWmmgibPdPfPMM2YyVv2+07VrV9Ojr734CxcuNJ/TOgmb05A0Iygdm6StQdoyr28sOkmGjmPWMqxI94xqT53OcqmJqPYua1mV9ujqm5zdJm1QWjasJcLas6it3PoFXsvEtDTXrj0eVq+4nVqzQ9GxMTqphCYbuhSZVkToc1XXPNZSfjvS56rGrRPQ6ERw2pCi5WI6K7ydhhc4lY611IY+nZhIv+Dr+dWlW7RBQlu37US/WFqVBlryHoyObyRpzhotc9dKFH196etMhxxpg0leL5+X32hVjw4jUP6VaP50SI/dkiT9TNDPCa1A0u8z+lmhzwlt1Nb3XWZTzxmt8tNZsrXnXnsXNfnUxEgnNNTngt16FLVzRZ8L2mipazLrT+0M0p58jdmuEwVqZZJTGtUbN25sPn/1c00TZe3M0KoEPcf6HShwThcniPLmxaK2AAAAAAA4kP0K9gEAAAAAsAmSZgAAAAAAQiBpBgAAAAAgBJJmAAAAAABCIGkGAAAAACAEkmYAAAAAAEIgaQYAAAAAIASSZgAAHMrr9Ypd2Tm2SHD6+XB6/ACQEzE5ujUAoMAYO3asjBs3LtPHt2vXToYPHy4DBgyQCy64QCZMmCB2tHPnTrnwwgtDXl+0aFEpXbq0nHPOOTJ48GBp3rx5jv5et27dZNeuXbJ48WIpW7Zstu7j6NGjMnr0aOnUqZN0797dt79BgwYm3lWrVkkk/fHHH/LSSy/Jxx9/nKv3O2rUKJk+fXqmj7/yyivlhRdekEjat2+fPPfcczJ06FBp2rSpOI3T4weA3EDSDADIFE3ILr300nQJpyZoNWrUkBYtWqS5rm7duuIkmmwGS54PHjwo69evl7lz58r3338vb731lnTu3FkiSRPBadOmSYcOHcSOrr32WomLi8v1+23ZsqWkpKSk2bdhwwbZuHGj1K9f3zxHA4+PtPvuu0+WLFkiQ4YMESdyevwA8H/tnX2sT3Ucx796tPU0U6S2FKIwLJqnpbE8zqqZPIW0lpIppJm0blLWoic1/vBYmFYaElrNs1sWykSXqaS75KmISwinvT7b9+z8jvO79/f7udd1eb+23373fs/5nd/34dzxPp/35/MtDSSahRBCZESHDh3sFQXhhmhu1qxZYkTv33//dUuWLHHXXHONu9CpUqWKmzBhQuIxxvHyyy+7zz//3L322msmoCtVquQuNKssc33ZZZddtFZexDivuAMC0cy9OWTIEHehUdFtzRW9/0IIURqU/7+sQgghLlqINhJxvvnmm11FH0deXp67/PLL3a5du9xvv/3mLkSY6zvuuKO8uyGEEEJcVEg0CyGEKDOwdWKZfeqpp1Iig7RxbOHChe6hhx5yjRo1srzn9957z50+fdodPXrUIrrk7N5zzz2uV69ebsOGDYnfgWW6X79+rmnTpmYR7969u5s3b16pR8iuvfZad/3114c5xVHoL33v2LGj5T63aNHCPfvss2YdzpSNGze6oUOHmvWb3FHG3a1bN/fhhx+6M2fOhOcxdz6v97nnngvn0h/zluSff/450VLvKSgosOOPPvpoSvv69evdwIEDLSeddeHzM2bMcP/991+JY8B54C3Sx44ds5/J4Y6ybt268PrMVadOnWzuioqKXFng+/Txxx/bPcX83HvvvSn5+YWFhW7UqFHuvvvus7mnz5z7999/J15z5cqV7umnn3atW7e283Fa9OnTxy1YsCAldYHv/e677+x37kt+p90fI0d7586dbvDgwXYNXlyX/sAXX3xhfx/k0XNvkRrA30ecTPvvv3fEiBH2Ge635s2b2/V79OhhDopM+i+EEJcasmcLIYQoF6ZMmeLWrFljIgaRifCbNGmSiS0E5O+//27H9u/fbxbwAQMGmCipU6dOeI133nnHhETlypVN4CFs+U/+6NGj3ffff28FjEqLzZs3u4MHD7orrrgiJZpLG8XOsAgTUUe4IFa++uorE1eMCfFfkrB78cUXLZKNWEb879692/34449u69atJnBeeuklOxcRu2nTJmtDZNWoUcPdeOONZ12Tebr77rtNHP/yyy9n5Zhj5fbX8yAsX3nlFesHgpbrshZY79euXWvF3Bh/Om677Ta73qJFi+y8zp07pxQ7mzlzpl0LCzkPObDEs07MEfM1a9asnIujlQTCnzllLRB+fj5YVwq8HT582PKimfsdO3ZYX5YvX+5mz57tbrnllvA6iG0e/HDPMQZSD3AeME+8Dh06ZPcqOfLMxTfffGN58TwMueGGG6ydexz43COPPGL3LeKVtVqxYoV9P+3vvvuu9Yfc9fz8fLvfSRUYNmxY2J9s+w8UokMEs878jdE/irfxoIfv6NKlS7H9F0KIS45ACCGEyJHPPvssqFu3bjBy5MjE4+vWrbPjAwcODNsmTpxobbyWLFkStq9cuTJs79KlS7B///7w2IgRI6x9/PjxYduqVausrWPHjsGuXbvC9r/++ivo1q2bHVu4cGGJYygsLLRz27Zte9axU6dOBQcOHAgWL14ctGnTxs4bO3ZsyjnDhg2z9ry8vODEiRNh++rVq4OGDRsGzZs3Dw4fPhy28z2cTz/h2LFjQdOmTYMmTZoEBQUFKdf2c9KoUaPg5MmTYTvzTfvSpUtTzqeN63imTp1qbe+///5ZY2vXrl3QoEGD4ODBg/b79u3b7feWLVsGW7ZsCc87evSorR/XmTRpUonzmdQP2Lx5c1CvXr2gWbNmwQ8//BC2Hz9+PBg+fLh95plnngmyxd9PvBd3j/LifvScPn3a1ov1oF/z588Pj505c8bmjM8MGDAgbP/jjz+C+vXrB61btw52796d8j1z5syx89u3b5/S3rdvX2tn/PF7jtfgwYPDtWWuWRfa6dOyZcvCz+Tn51t7q1atwrZs+x/9Xua6qKgoPMba0t69e/cS+y+EEJcasmcLIYQoF7DnEon03H///WEUi62qotFTX4CM6HM0aglERolweohUYk0FrM2ZQvQN+2n0Vb9+fdeqVSuL7B04cMBs4CNHjgw/s2fPHrd06VJXs2ZNi25fddVV4TEiztjKiURTQCwdXJexY1m+6667Uo7RTvT6+PHjdp1s6dq1q0V1o7ZbH50k2krUle20gMgkFuznn3/eNWjQIDyXNWE+GRvnRK3i2UDEE8s80cxopfWrr77avf766+6mm24yq310jUuTO++806K5Hj8vrDsW6Icffjg8RpE3LNNE6om0ErkFIq4PPPCA2eKJ8Echcss1iWZnA7bqK6+8MpxrX5md96i1nfuQiDT3C+kAkG3/o1DYLlqgz9v0sfULIYRIRaJZCCFEuYCdOo4XcHHxeN1119n7iRMn7J28TqywWICxyMZBLFStWtX99NNPocAoCW9H5YXYxJ7sBQg52atWrTKLtBc4Pv8XEYmlOtru8bZsnxeaBNt1vfXWW27QoEFhG9sqYanGju7HnElOcZzq1atb/i7Wca4Xt2Y/+OCDYZvvY1RYehC0rAmi8ddff3W54HPS4xXYAatz27ZtU84rbbAuxyluzKw7OcvR87gnyL/GOu05efKk5a5jscfunM06cY/eeuutKW1Y1pP+BsDn1Pt7Itv+R+8LXvFr82AE+7cQQohUlNMshBCiXCA/Mo7fxil+LL69E3mjRF+BwkfFQU50JlteJW05RZ4tUWbyYYmOxotaEWkGCnP54lxJ7N27t9jvJgK7bNkyE8kIMKKVfj9iP/ZcC5vxEIB8cSLiRPC5Dj/zkCA6Hj+WpL2q42OJ5pVnCuvAg4W4WPN48UgktSzwgjOKHzPRXl7p2LdvX/gzopgCXcwhEVyukWv0PalP6f4GosfOpf/Rh1BxeAjFQwAhhBCpSDQLIYQoF4jK5YqvIIwYjgvZOFHLdLYQFR0+fLh788037Z2q3FHB6MUSUUHsv+mIRxPjY6Fi8urVq82qTDSTCCH2cCzs2JnPxTJL1eVXX33VffnllyaaidAjtrD0EuGN9gNRRpS9OJLEXCaUJPr9XJ7LehVH0v7V/jtxBPgIbxK1atWyd1wLWPQpzsa9h1uChwysP8XsmLtsIrXFFVXLhGz77ynPPcaFEKIiItEshBCiwoGNm6glQiweHS5tHn/8cff1119bBW+2CPrkk09CAYZtGbCIkyOaC+Q7I5ipYjx58uSzxA9Vkc8Fopls50XUHGs14hniW1FVq1bN8mOp4l0WFay5PnnURKqTos1+K6Oyqp6dhF8/7NZsfVUSOA4QzOQ1c9+xf7eHCO35tjZn238hhBC5oZxmIYQQFQ6ikURk2bonKQcWYUaE9cknnzxnuykCmUgtUUG2gGJbJo/Pp/72228Tc1kpfuX3OU4HW/1Az549zxLM2H+9tTYaqc02UugFMltg8QCAXFoKS0UhLxsQ8HGwwhOZ7tu3r+U154KfK8R7HHJ0yRkHttE6X/g+JY0Z2M+YAl+sb3StHnvssRTBDBTc8uRq1y7r/gshhMgNiWYhhBAVEmyyQHGunTt3hu1E+6hkzR64iNDSsPtSRArBCOydyz7MQNVsqhwTwaUCdFSgE5GkaBRFuJKKOnmojg2IxqjYooo0lnCPL/4EfkxHjhzJqP9EmsljpZo41mz24Y3b4xkfYnz8+PFWXdtDbvXYsWPdtm3bTLgjuEsCmzn9jc4H1+cBxMSJE0PxCZzDGpLLTDGw+J7CZQnzwHgo4hXPSceKz37TFFDzefN+rdhLOcqWLVtcXl5e4loxF1BUVFTu/c+Fsuy/EEJUFGTPFkIIUSFBMFDgisgvVaDJLyXfFhs1opbcY6zGpcWQIUPc4sWLraAV1txx48ZZO9sxIQjnzp1rWyYhUMh9JXeYPGGi3S1btkx7XbYKmj59uhWWKigosFxmormMA5HJdloIaL7X51Pffvvt9k7V7TVr1piFHHt3OhDZRN4RUknWbKDQGSKda7JVFuPAUo0g/PPPP80K/MYbb2Q0VzxM4GEB16ldu7YJcdaHyCf54b1797YoKQ81GCfRdHLCEefnE/KSfeVyrPdTpkyx/N/CwkJ7SIC74O233w4LZ/Xp08fEKetFZJlxMjc8ZGA7KGznuBx4AEBVdL9WrBH3Iu6IF154odRyirPtfy4k9d+PTQghLhUUaRZCCFFhGTNmjIkCBB8iASFD5I09ahHTfgur0gBRhGAAInubNm2ynxFKiFH2WUbE5OfnW+EuROEHH3xgQrE4iF7OmTPHCkohtpcvX26ihxxVrosgjkc3EaMUnSJKi6BBoJaEF8qI8MaNGyeewximTZtm1m0i9dh+KRbWv39/E4uZiiXEL9F1+rV27Vr3zz//WPsTTzxhgpOHCKwX0XUedAwdOtRyxX2O7vmEvrCePLwgco+FnTxyHjJ8+umn4VZYfiuzjz76yD5DxJ614gEHOcVUPvdzTLsHQYsbgX22uTdy3bKrNPqfC2XdfyGEqAhUCnLdw0IIIYQQQgghhLjIUaRZCCGEEEIIIYRIg0SzEEIIIYQQQgiRBolmIYQQQgghhBAiDRLNQgghhBBCCCFEGiSahRBCCCGEEEKINEg0CyGEEEIIIYQQaZBoFkIIIYQQQggh0iDRLIQQQgghhBBCpEGiWQghhBBCCCGESINEsxBCCCGEEEIIkQaJZiGEEEIIIYQQIg0SzUIIIYQQQgghRBokmoUQQgghhBBCCJfM/76wPkmiPDLVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 5.719  p-value: 0.221\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(5): 10.722  p-value: 0.057\n",
      "\n",
      "--- Running Placebo Test for Uganda - cci ---\n",
      "Treatment cohorts: {2012.0: np.int64(1013), 2014.0: np.int64(260), 2016.0: np.int64(161), 2020.0: np.int64(83)}\n",
      "\n",
      "Testing placebo treatment at t=2010 for cohort actually treated at t=2012.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2012 for cohort actually treated at t=2014.0\n",
      "  Coefficient: 0.3034 (SE: 0.1085)  p=0.0061  ⚠️ FAIL\n",
      "\n",
      "Testing placebo treatment at t=2014 for cohort actually treated at t=2016.0\n",
      "  Coefficient: -0.0065 (SE: 0.0970)  p=0.9467  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2020.0\n",
      "  Coefficient: -0.0992 (SE: 0.1422)  p=0.4865  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 2/3 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2014.0          2012  0.303353  0.108494  0.006098         60        174   \n",
      "1  2016.0          2014 -0.006489  0.096981  0.946724         35        174   \n",
      "2  2020.0          2016 -0.099160  0.142239  0.486542         18        174   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "2  DID-pre-only  \n",
      "❌ Overall Result: 1/3 placebo tests failed. Suggests presence of pre-trends.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 2: Treatment defined by 'cash_sale' *****\n",
      "***** (Among CCI sellers: Never cash vs Late cash) *****\n",
      "**************************************************\n",
      "  Restricted to CCI sellers: 3215 households\n",
      "\n",
      "  Filtering to households present at baseline year 2010\n",
      "  Households with baseline data: 1829\n",
      "  Note: This analysis is restricted to CCI sellers\n",
      "\n",
      "  Treatment assignment for cash_sale (among CCI sellers):\n",
      "  Control: CCI sellers who never sell cash crops\n",
      "  Treatment: CCI sellers who start selling cash crops after baseline\n",
      "  Baseline year: 2010\n",
      "  Total households in data: 1829\n",
      "  Control group: 414 households\n",
      "  Treatment group: 443 households\n",
      "  Excluded: 972 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 857\n",
      "  Total observations at baseline: 857\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 414 households\n",
      "    Treatment (1): 443 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 414\n",
      "  Treatment households: 443\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 682/857 (79.6%)\n",
      "  Mean SMD: 0.066\n",
      "  Max SMD: 0.200\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 816/857 (95.2%)\n",
      "  Mean SMD: 0.097\n",
      "  Max SMD: 0.225\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 850/857 (99.2%)\n",
      "  Mean SMD: 0.104\n",
      "  Max SMD: 0.242\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 682/857 (79.6%)\n",
      "  Mean SMD: 0.074\n",
      "  Max SMD: 0.171\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 805/857 (93.9%)\n",
      "  Mean SMD: 0.092\n",
      "  Max SMD: 0.180\n",
      "\n",
      "kernel:\n",
      "  Matched: 857/857 (100.0%)\n",
      "  Mean SMD: 0.109\n",
      "  Max SMD: 0.251\n",
      "\n",
      "radius:\n",
      "  Matched: 857/857 (100.0%)\n",
      "  Mean SMD: 0.109\n",
      "  Max SMD: 0.251\n",
      "\n",
      "stratification:\n",
      "  Matched: 857/857 (100.0%)\n",
      "  Mean SMD: 0.109\n",
      "  Max SMD: 0.251\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Uganda: DID Results (from 'cash_sale' analysis among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Uganda - Cash Sale (CCI sellers: never cash vs late cash)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0     1   All\n",
      "treatment_group                  \n",
      "0.0               735     0   735\n",
      "1.0               710  1109  1819\n",
      "All              1445  1109  2554\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.245769, unique_values=2\n",
      "  treatment_group: variance=0.205045, unique_values=2\n",
      "  post: variance=0.245769, unique_values=2\n",
      "  emp: variance=0.139932, unique_values=2\n",
      "  vharvest: variance=119142703169536.000000, unique_values=858\n",
      "  lognf: variance=21.699812, unique_values=1148\n",
      "  educh: variance=0.144190, unique_values=2\n",
      "  head_age: variance=245.663635, unique_values=78\n",
      "  head_sex: variance=0.219863, unique_values=2\n",
      "  dependency_ratio: variance=2.994855, unique_values=80\n",
      "  farmsize: variance=9.221626, unique_values=825\n",
      "  asset: variance=1.316996, unique_values=343\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 2554\n",
      "  Unique households: 682\n",
      "  Unique time periods: 5\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 735 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 710 obs\n",
      "     Treatment=1, Post=1: 1109 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 2260/2554 (88.5%)\n",
      "  Mean: 0.832, Std: 0.374\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0098\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -2.7474\n",
      "No. Observations:                2204   R-squared (Within):               0.0033\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.2864\n",
      "Time:                        15:47:38   Log-likelihood                   -327.25\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      1.6760\n",
      "Entities:                         670   P-value                           0.0897\n",
      "Avg Obs:                       3.2896   Distribution:                  F(9,1521)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             5371.1\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       5   Distribution:                  F(9,1521)\n",
      "Avg Obs:                       440.80                                           \n",
      "Min Obs:                       339.00                                           \n",
      "Max Obs:                       658.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post          -0.0603     0.0340    -1.7713     0.0767     -0.1270      0.0065\n",
      "treatment_group      1.0506     0.1289     8.1476     0.0000      0.7976      1.3035\n",
      "vharvest          7.017e-10  2.815e-10     2.4925     0.0128   1.495e-10   1.254e-09\n",
      "lognf                0.0038     0.0024     1.5599     0.1190     -0.0010      0.0086\n",
      "educh                0.0233     0.0453     0.5142     0.6072     -0.0656      0.1122\n",
      "head_age             0.0008     0.0015     0.5478     0.5839     -0.0022      0.0038\n",
      "head_sex            -0.0423     0.0419    -1.0097     0.3128     -0.1245      0.0399\n",
      "dependency_ratio    -0.0067     0.0064    -1.0471     0.2952     -0.0193      0.0059\n",
      "farmsize             0.0064     0.0040     1.5836     0.1135     -0.0015      0.0144\n",
      "asset               -0.0108     0.0124    -0.8671     0.3860     -0.0352      0.0136\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 2.1254\n",
      "P-value: 0.0000\n",
      "Distribution: F(673,1521)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Uganda - cash_sale ---\n",
      "Event times for treated units: [-10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m10.0 + treat_event_m8.0 + treat_event_m6.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_4.0 + treat_event_6.0 + treat_event_8.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0151\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0982\n",
      "No. Observations:                2260   R-squared (Within):              -0.0030\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.1257\n",
      "Time:                        15:47:38   Log-likelihood                   -338.39\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.6851\n",
      "Entities:                         673   P-value                           0.0042\n",
      "Avg Obs:                       3.3581   Distribution:                  F(9,1574)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             1.8550\n",
      "                                        P-value                           0.0546\n",
      "Time periods:                       5   Distribution:                  F(9,1574)\n",
      "Avg Obs:                       452.00                                           \n",
      "Min Obs:                       345.00                                           \n",
      "Max Obs:                       662.00                                           \n",
      "                                                                                \n",
      "                                 Parameter Estimates                                 \n",
      "=====================================================================================\n",
      "                   Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-------------------------------------------------------------------------------------\n",
      "treat_event_m10.0    -0.0528     0.1233    -0.4283     0.6685     -0.2946      0.1890\n",
      "treat_event_m8.0     -0.1818     0.1445    -1.2580     0.2086     -0.4653      0.1017\n",
      "treat_event_m6.0     -0.1369     0.0638    -2.1452     0.0321     -0.2620     -0.0117\n",
      "treat_event_m4.0     -0.0797     0.0485    -1.6426     0.1007     -0.1750      0.0155\n",
      "treat_event_0.0      -0.0769     0.0368    -2.0917     0.0366     -0.1490     -0.0048\n",
      "treat_event_2.0      -0.0897     0.0456    -1.9666     0.0494     -0.1792     -0.0002\n",
      "treat_event_4.0      -0.1456     0.0470    -3.0971     0.0020     -0.2378     -0.0534\n",
      "treat_event_6.0      -0.0974     0.0609    -1.5991     0.1100     -0.2170      0.0221\n",
      "treat_event_8.0      -0.1471     0.0603    -2.4418     0.0147     -0.2653     -0.0289\n",
      "=====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.7551\n",
      "P-value: 0.0000\n",
      "Distribution: F(676,1574)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Uganda_cash_sale.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 6.027  p-value: 0.197\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(5): 10.408  p-value: 0.064\n",
      "\n",
      "--- Running Placebo Test for Uganda - cash_sale ---\n",
      "Treatment cohorts: {2012.0: np.int64(1028), 2014.0: np.int64(327), 2016.0: np.int64(331), 2020.0: np.int64(133)}\n",
      "\n",
      "Testing placebo treatment at t=2010 for cohort actually treated at t=2012.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2012 for cohort actually treated at t=2014.0\n",
      "  Coefficient: 0.1132 (SE: 0.0849)  p=0.1843  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2014 for cohort actually treated at t=2016.0\n",
      "  Coefficient: 0.0357 (SE: 0.0669)  p=0.5941  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2020.0\n",
      "  Coefficient: -0.0324 (SE: 0.1130)  p=0.7745  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 3/3 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2014.0          2012  0.113202  0.084923  0.184337         73        239   \n",
      "1  2016.0          2014  0.035712  0.066941  0.594051         69        239   \n",
      "2  2020.0          2016 -0.032402  0.113024  0.774528         29        239   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "2  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 3: Treatment defined by 'cc' *****\n",
      "***** (Among CCI sellers: Never certified vs Late certified) *****\n",
      "**************************************************\n",
      "  Filtered to CCI sellers only: 3215 households\n",
      "\n",
      "  Filtering to households present at baseline year 2010\n",
      "  Households with baseline data: 1439\n",
      "\n",
      "  Treatment assignment for cc:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2010\n",
      "  Total households in data: 1439\n",
      "  Control group: 256 households\n",
      "  Treatment group: 208 households\n",
      "  Excluded: 975 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 464\n",
      "  Total observations at baseline: 464\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 256 households\n",
      "    Treatment (1): 208 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 256\n",
      "  Treatment households: 208\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 329/464 (70.9%)\n",
      "  Mean SMD: 0.087\n",
      "  Max SMD: 0.199\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 423/464 (91.2%)\n",
      "  Mean SMD: 0.069\n",
      "  Max SMD: 0.172\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 443/464 (95.5%)\n",
      "  Mean SMD: 0.092\n",
      "  Max SMD: 0.211\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 335/464 (72.2%)\n",
      "  Mean SMD: 0.098\n",
      "  Max SMD: 0.227\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 413/464 (89.0%)\n",
      "  Mean SMD: 0.120\n",
      "  Max SMD: 0.260\n",
      "\n",
      "kernel:\n",
      "  Matched: 464/464 (100.0%)\n",
      "  Mean SMD: 0.133\n",
      "  Max SMD: 0.413\n",
      "\n",
      "radius:\n",
      "  Matched: 460/464 (99.1%)\n",
      "  Mean SMD: 0.118\n",
      "  Max SMD: 0.317\n",
      "\n",
      "stratification:\n",
      "  Matched: 464/464 (100.0%)\n",
      "  Mean SMD: 0.133\n",
      "  Max SMD: 0.413\n",
      "\n",
      "Selected method: nearest_neighbor_3\n",
      "\n",
      "--- Uganda: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Uganda - CC (CCI sellers: never certified vs late certified)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1   All\n",
      "treatment_group                \n",
      "0.0              398    0   398\n",
      "1.0              270  429   699\n",
      "All              668  429  1097\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2010), np.int64(2012), np.int64(2014), np.int64(2016), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.238351, unique_values=2\n",
      "  treatment_group: variance=0.231389, unique_values=2\n",
      "  post: variance=0.238351, unique_values=2\n",
      "  emp: variance=0.140349, unique_values=2\n",
      "  vharvest: variance=272681618374656.000000, unique_values=634\n",
      "  lognf: variance=19.180990, unique_values=642\n",
      "  educh: variance=0.126624, unique_values=2\n",
      "  head_age: variance=230.366364, unique_values=75\n",
      "  head_sex: variance=0.201769, unique_values=2\n",
      "  dependency_ratio: variance=2.758308, unique_values=63\n",
      "  farmsize: variance=4.856597, unique_values=523\n",
      "  asset: variance=1.045342, unique_values=205\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 1097\n",
      "  Unique households: 423\n",
      "  Unique time periods: 5\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 398 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 270 obs\n",
      "     Treatment=1, Post=1: 429 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 931/1097 (84.9%)\n",
      "  Mean: 0.831, Std: 0.375\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0178\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -3.9813\n",
      "No. Observations:                 926   R-squared (Within):               0.0102\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -2.5426\n",
      "Time:                        15:47:39   Log-likelihood                   -33.150\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      1.0226\n",
      "Entities:                         406   P-value                           0.4205\n",
      "Avg Obs:                       2.2808   Distribution:                   F(9,507)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             24.430\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       5   Distribution:                   F(9,507)\n",
      "Avg Obs:                       185.20                                           \n",
      "Min Obs:                       106.00                                           \n",
      "Max Obs:                       402.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0010     0.0705     0.0143     0.9886     -0.1375      0.1395\n",
      "treatment_group      1.3298     0.3035     4.3812     0.0000      0.7335      1.9262\n",
      "vharvest          7.473e-10  3.111e-10     2.4023     0.0167   1.361e-10   1.358e-09\n",
      "lognf                0.0058     0.0054     1.0754     0.2827     -0.0048      0.0163\n",
      "educh                0.0018     0.0690     0.0257     0.9795     -0.1339      0.1374\n",
      "head_age            -0.0036     0.0038    -0.9720     0.3315     -0.0110      0.0037\n",
      "head_sex             0.0261     0.0878     0.2970     0.7666     -0.1463      0.1985\n",
      "dependency_ratio    -0.0069     0.0107    -0.6496     0.5162     -0.0280      0.0141\n",
      "farmsize             0.0092     0.0104     0.8772     0.3808     -0.0114      0.0297\n",
      "asset               -0.0401     0.0288    -1.3914     0.1647     -0.0968      0.0165\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.7348\n",
      "P-value: 0.0000\n",
      "Distribution: F(409,507)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Uganda - cc ---\n",
      "Event times for treated units: [-10.0, -8.0, -6.0, -4.0, -2.0, 0.0, 2.0, 4.0, 6.0, 8.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m10.0 + treat_event_m8.0 + treat_event_m6.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_4.0 + treat_event_6.0 + treat_event_8.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0355\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0401\n",
      "No. Observations:                 931   R-squared (Within):              -0.0744\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.0808\n",
      "Time:                        15:47:39   Log-likelihood                   -24.019\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.0953\n",
      "Entities:                         406   P-value                           0.0284\n",
      "Avg Obs:                       2.2931   Distribution:                   F(9,512)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       5.0000   F-statistic (robust):             1.2704\n",
      "                                        P-value                           0.2502\n",
      "Time periods:                       5   Distribution:                   F(9,512)\n",
      "Avg Obs:                       186.20                                           \n",
      "Min Obs:                       110.00                                           \n",
      "Max Obs:                       402.00                                           \n",
      "                                                                                \n",
      "                                 Parameter Estimates                                 \n",
      "=====================================================================================\n",
      "                   Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "-------------------------------------------------------------------------------------\n",
      "treat_event_m10.0     0.2759     0.1756     1.5710     0.1168     -0.0691      0.6209\n",
      "treat_event_m8.0      0.1411     0.2660     0.5306     0.5959     -0.3815      0.6637\n",
      "treat_event_m6.0     -0.0352     0.1303    -0.2706     0.7868     -0.2912      0.2207\n",
      "treat_event_m4.0      0.0515     0.1005     0.5124     0.6086     -0.1460      0.2490\n",
      "treat_event_0.0       0.0188     0.0759     0.2477     0.8045     -0.1302      0.1678\n",
      "treat_event_2.0      -0.0987     0.1008    -0.9791     0.3280     -0.2968      0.0994\n",
      "treat_event_4.0      -0.2271     0.1037    -2.1912     0.0289     -0.4307     -0.0235\n",
      "treat_event_6.0      -0.2028     0.1483    -1.3673     0.1721     -0.4941      0.0886\n",
      "treat_event_8.0      -0.3271     0.1428    -2.2911     0.0224     -0.6075     -0.0466\n",
      "=====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.5812\n",
      "P-value: 0.0000\n",
      "Distribution: F(409,512)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Uganda_cc.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 4.055  p-value: 0.399\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(5): 8.624  p-value: 0.125\n",
      "\n",
      "--- Running Placebo Test for Uganda - cc ---\n",
      "Treatment cohorts: {2012.0: np.int64(435), 2014.0: np.int64(118), 2016.0: np.int64(90), 2020.0: np.int64(56)}\n",
      "\n",
      "Testing placebo treatment at t=2010 for cohort actually treated at t=2012.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2012 for cohort actually treated at t=2014.0\n",
      "  Coefficient: -0.0140 (SE: 0.1240)  p=0.9108  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2014 for cohort actually treated at t=2016.0\n",
      "  Coefficient: 0.0733 (SE: 0.2158)  p=0.7348  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2020.0\n",
      "  Coefficient: -0.2376 (SE: 0.2262)  p=0.2958  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 3/3 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2014.0          2012 -0.013953  0.124011  0.910815         30        215   \n",
      "1  2016.0          2014  0.073333  0.215825  0.734796         27        215   \n",
      "2  2020.0          2016 -0.237635  0.226183  0.295773         16        215   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "2  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "============================================================\n",
      "********** NOW RUNNING: Tanzania **********\n",
      "============================================================\n",
      "\n",
      "Tanzania data loaded:\n",
      "  Observations: 10830\n",
      "  Time periods: [np.int64(2009), np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "  Number of households: 4067\n",
      "  'cc' variable found with 10830 non-missing values\n",
      "  Note: For Tanzania, analysis is restricted to start from the second wave: 2011.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 1: Treatment defined by 'cci' *****\n",
      "***** (All households: Never sellers vs Late sellers) *****\n",
      "**************************************************\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 2205\n",
      "\n",
      "  Treatment assignment for cci:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 2205\n",
      "  Control group: 418 households\n",
      "  Treatment group: 535 households\n",
      "  Excluded: 1252 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 953\n",
      "  Total observations at baseline: 953\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 418 households\n",
      "    Treatment (1): 535 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 418\n",
      "  Treatment households: 535\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 781/953 (82.0%)\n",
      "  Mean SMD: 0.042\n",
      "  Max SMD: 0.106\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 927/953 (97.3%)\n",
      "  Mean SMD: 0.090\n",
      "  Max SMD: 0.184\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 951/953 (99.8%)\n",
      "  Mean SMD: 0.100\n",
      "  Max SMD: 0.204\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 799/953 (83.8%)\n",
      "  Mean SMD: 0.059\n",
      "  Max SMD: 0.215\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 907/953 (95.2%)\n",
      "  Mean SMD: 0.089\n",
      "  Max SMD: 0.190\n",
      "\n",
      "kernel:\n",
      "  Matched: 953/953 (100.0%)\n",
      "  Mean SMD: 0.105\n",
      "  Max SMD: 0.212\n",
      "\n",
      "radius:\n",
      "  Matched: 953/953 (100.0%)\n",
      "  Mean SMD: 0.105\n",
      "  Max SMD: 0.212\n",
      "\n",
      "stratification:\n",
      "  Matched: 953/953 (100.0%)\n",
      "  Mean SMD: 0.105\n",
      "  Max SMD: 0.212\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Tanzania: DID Results (from 'cci' analysis) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Tanzania - CCI (never sellers vs late sellers)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               487    0   487\n",
      "1.0               754  902  1656\n",
      "All              1241  902  2143\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.243858, unique_values=2\n",
      "  treatment_group: variance=0.175690, unique_values=2\n",
      "  post: variance=0.243858, unique_values=2\n",
      "  emp: variance=0.249975, unique_values=2\n",
      "  lognf: variance=36.748497, unique_values=890\n",
      "  educh: variance=0.225155, unique_values=2\n",
      "  head_age: variance=280.327454, unique_values=81\n",
      "  head_sex: variance=0.184576, unique_values=2\n",
      "  dependency_ratio: variance=0.956029, unique_values=58\n",
      "  vharvest: variance=1091207757824.000000, unique_values=972\n",
      "  farmsize: variance=13.235001, unique_values=999\n",
      "  asset: variance=3.725534, unique_values=1077\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 2143\n",
      "  Unique households: 781\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 487 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 754 obs\n",
      "     Treatment=1, Post=1: 902 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 2143/2143 (100.0%)\n",
      "  Mean: 0.512, Std: 0.500\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + lognf + educh + head_age + head_sex + dependency_ratio + vharvest + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.1153\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.1952\n",
      "No. Observations:                1747   R-squared (Within):               0.0045\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.5485\n",
      "Time:                        15:47:42   Log-likelihood                   -445.06\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      13.827\n",
      "Entities:                         781   P-value                           0.0000\n",
      "Avg Obs:                       2.2369   Distribution:                   F(9,955)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             49.394\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                   F(9,955)\n",
      "Avg Obs:                       582.33                                           \n",
      "Min Obs:                       373.00                                           \n",
      "Max Obs:                       781.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.2685     0.0554     4.8425     0.0000      0.1597      0.3772\n",
      "treatment_group      0.8242     0.1700     4.8470     0.0000      0.4905      1.1578\n",
      "lognf             5.313e-05     0.0036     0.0146     0.9883     -0.0071      0.0072\n",
      "educh                0.0932     0.0556     1.6757     0.0941     -0.0160      0.2024\n",
      "head_age             0.0014     0.0018     0.7768     0.4375     -0.0021      0.0049\n",
      "head_sex            -0.4151     0.0680    -6.1054     0.0000     -0.5485     -0.2817\n",
      "dependency_ratio    -0.0079     0.0229    -0.3440     0.7309     -0.0528      0.0371\n",
      "vharvest          9.304e-09  3.338e-08     0.2787     0.7805   -5.62e-08   7.481e-08\n",
      "farmsize             0.0059     0.0058     1.0194     0.3083     -0.0055      0.0174\n",
      "asset                0.0225     0.0149     1.5066     0.1322     -0.0068      0.0517\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.8034\n",
      "P-value: 0.0000\n",
      "Distribution: F(782,955)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Tanzania - cci ---\n",
      "Event times for treated units: [-9.0, -7.0, -5.0, -4.0, -2.0, 0.0, 2.0, 5.0, 7.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m9.0 + treat_event_m7.0 + treat_event_m5.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_5.0 + treat_event_7.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0333\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.1947\n",
      "No. Observations:                2143   R-squared (Within):              -0.0415\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.1473\n",
      "Time:                        15:47:42   Log-likelihood                   -886.41\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      5.8217\n",
      "Entities:                         781   P-value                           0.0000\n",
      "Avg Obs:                       2.7439   Distribution:                  F(8,1351)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             4.0134\n",
      "                                        P-value                           0.0001\n",
      "Time periods:                       4   Distribution:                  F(8,1351)\n",
      "Avg Obs:                       535.75                                           \n",
      "Min Obs:                       378.00                                           \n",
      "Max Obs:                       781.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m9.0     0.0662     0.1687     0.3926     0.6947     -0.2647      0.3971\n",
      "treat_event_m7.0     0.1234     0.1796     0.6872     0.4921     -0.2289      0.4757\n",
      "treat_event_m5.0     0.2530     0.2066     1.2245     0.2210     -0.1523      0.6583\n",
      "treat_event_m4.0    -0.0347     0.0722    -0.4801     0.6313     -0.1763      0.1070\n",
      "treat_event_0.0      0.2750     0.0582     4.7233     0.0000      0.1608      0.3893\n",
      "treat_event_2.0      0.1936     0.1297     1.4923     0.1358     -0.0609      0.4481\n",
      "treat_event_5.0      0.0525     0.1406     0.3731     0.7091     -0.2234      0.3284\n",
      "treat_event_7.0      0.0469     0.1534     0.3060     0.7596     -0.2539      0.3478\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.4511\n",
      "P-value: 0.0000\n",
      "Distribution: F(783,1351)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Tanzania_cci.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 1.696  p-value: 0.791\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(4): 27.616  p-value: 0.000\n",
      "\n",
      "--- Running Placebo Test for Tanzania - cci ---\n",
      "Treatment cohorts: {2013.0: np.int64(734), 2015.0: np.int64(691), 2020.0: np.int64(231)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2015.0\n",
      "  Coefficient: 0.0062 (SE: 0.0663)  p=0.9262  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2015 for cohort actually treated at t=2020.0\n",
      "  Coefficient: 0.2568 (SE: 0.1531)  p=0.0945  ⚠️ FAIL\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2015.0          2013  0.006154  0.066347  0.926167        204        246   \n",
      "1  2020.0          2015  0.256835  0.153069  0.094506         71        246   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "❌ Overall Result: 1/2 placebo tests failed. Suggests presence of pre-trends.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 2: Treatment defined by 'cash_sale' *****\n",
      "***** (Among CCI sellers: Never cash vs Late cash) *****\n",
      "**************************************************\n",
      "  Restricted to CCI sellers: 2949 households\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 1787\n",
      "  Note: This analysis is restricted to CCI sellers\n",
      "\n",
      "  Treatment assignment for cash_sale (among CCI sellers):\n",
      "  Control: CCI sellers who never sell cash crops\n",
      "  Treatment: CCI sellers who start selling cash crops after baseline\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 1787\n",
      "  Control group: 383 households\n",
      "  Treatment group: 490 households\n",
      "  Excluded: 914 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 873\n",
      "  Total observations at baseline: 873\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 383 households\n",
      "    Treatment (1): 490 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 383\n",
      "  Treatment households: 490\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 713/873 (81.7%)\n",
      "  Mean SMD: 0.049\n",
      "  Max SMD: 0.217\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 839/873 (96.1%)\n",
      "  Mean SMD: 0.088\n",
      "  Max SMD: 0.257\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 865/873 (99.1%)\n",
      "  Mean SMD: 0.105\n",
      "  Max SMD: 0.273\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 728/873 (83.4%)\n",
      "  Mean SMD: 0.043\n",
      "  Max SMD: 0.166\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 836/873 (95.8%)\n",
      "  Mean SMD: 0.085\n",
      "  Max SMD: 0.215\n",
      "\n",
      "kernel:\n",
      "  Matched: 873/873 (100.0%)\n",
      "  Mean SMD: 0.114\n",
      "  Max SMD: 0.285\n",
      "\n",
      "radius:\n",
      "  Matched: 872/873 (99.9%)\n",
      "  Mean SMD: 0.109\n",
      "  Max SMD: 0.282\n",
      "\n",
      "stratification:\n",
      "  Matched: 873/873 (100.0%)\n",
      "  Mean SMD: 0.114\n",
      "  Max SMD: 0.285\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Tanzania: DID Results (from 'cash_sale' analysis among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Tanzania - Cash Sale (CCI sellers: never cash vs late cash)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               595    0   595\n",
      "1.0               710  853  1563\n",
      "All              1305  853  2158\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.239143, unique_values=2\n",
      "  treatment_group: variance=0.199790, unique_values=2\n",
      "  post: variance=0.239143, unique_values=2\n",
      "  emp: variance=0.247469, unique_values=2\n",
      "  vharvest: variance=3140567433216.000000, unique_values=1057\n",
      "  lognf: variance=36.020813, unique_values=900\n",
      "  educh: variance=0.217272, unique_values=2\n",
      "  head_age: variance=258.174744, unique_values=78\n",
      "  head_sex: variance=0.180562, unique_values=2\n",
      "  dependency_ratio: variance=0.963863, unique_values=60\n",
      "  farmsize: variance=12.771018, unique_values=1073\n",
      "  asset: variance=2.926186, unique_values=1062\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 2158\n",
      "  Unique households: 728\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 595 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 710 obs\n",
      "     Treatment=1, Post=1: 853 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 2158/2158 (100.0%)\n",
      "  Mean: 0.551, Std: 0.497\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0464\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -2.2741\n",
      "No. Observations:                1735   R-squared (Within):               0.0236\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.0033\n",
      "Time:                        15:47:44   Log-likelihood                   -543.56\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      5.3849\n",
      "Entities:                         728   P-value                           0.0000\n",
      "Avg Obs:                       2.3832   Distribution:                   F(9,996)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             46.677\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                   F(9,996)\n",
      "Avg Obs:                       578.33                                           \n",
      "Min Obs:                       428.00                                           \n",
      "Max Obs:                       728.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0599     0.0537     1.1153     0.2650     -0.0455      0.1652\n",
      "treatment_group      1.0792     0.1839     5.8674     0.0000      0.7183      1.4402\n",
      "vharvest          1.204e-08  2.816e-08     0.4276     0.6690  -4.322e-08   6.731e-08\n",
      "lognf               -0.0029     0.0035    -0.8326     0.4053     -0.0097      0.0039\n",
      "educh                0.0207     0.0585     0.3539     0.7235     -0.0940      0.1354\n",
      "head_age             0.0004     0.0019     0.2260     0.8212     -0.0032      0.0041\n",
      "head_sex            -0.2777     0.0639    -4.3435     0.0000     -0.4031     -0.1522\n",
      "dependency_ratio    -0.0029     0.0230    -0.1242     0.9012     -0.0479      0.0422\n",
      "farmsize             0.0009     0.0070     0.1240     0.9013     -0.0128      0.0145\n",
      "asset                0.0212     0.0148     1.4379     0.1508     -0.0077      0.0502\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.7409\n",
      "P-value: 0.0000\n",
      "Distribution: F(729,996)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Tanzania - cash_sale ---\n",
      "Event times for treated units: [-9.0, -7.0, -5.0, -4.0, -2.0, 0.0, 2.0, 5.0, 7.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m9.0 + treat_event_m7.0 + treat_event_m5.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_5.0 + treat_event_7.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0120\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0054\n",
      "No. Observations:                2158   R-squared (Within):              -0.0173\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.0128\n",
      "Time:                        15:47:44   Log-likelihood                   -917.74\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.1480\n",
      "Entities:                         728   P-value                           0.0289\n",
      "Avg Obs:                       2.9643   Distribution:                  F(8,1419)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             1.3002\n",
      "                                        P-value                           0.2390\n",
      "Time periods:                       4   Distribution:                  F(8,1419)\n",
      "Avg Obs:                       539.50                                           \n",
      "Min Obs:                       419.00                                           \n",
      "Max Obs:                       728.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m9.0    -0.1388     0.1285    -1.0797     0.2805     -0.3909      0.1134\n",
      "treat_event_m7.0    -0.1616     0.1393    -1.1599     0.2463     -0.4348      0.1117\n",
      "treat_event_m5.0    -0.1663     0.1685    -0.9869     0.3238     -0.4968      0.1642\n",
      "treat_event_m4.0    -0.0623     0.0665    -0.9377     0.3486     -0.1927      0.0681\n",
      "treat_event_0.0      0.0527     0.0536     0.9834     0.3256     -0.0525      0.1579\n",
      "treat_event_2.0     -0.0630     0.1105    -0.5700     0.5688     -0.2796      0.1537\n",
      "treat_event_5.0      0.0743     0.0988     0.7521     0.4521     -0.1195      0.2681\n",
      "treat_event_7.0     -0.0277     0.1147    -0.2415     0.8092     -0.2526      0.1972\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.5285\n",
      "P-value: 0.0000\n",
      "Distribution: F(730,1419)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Tanzania_cash_sale.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 2.815  p-value: 0.589\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(4): 3.249  p-value: 0.517\n",
      "\n",
      "--- Running Placebo Test for Tanzania - cash_sale ---\n",
      "Treatment cohorts: {2013.0: np.int64(666), 2015.0: np.int64(676), 2020.0: np.int64(221)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2015.0\n",
      "  Coefficient: 0.1236 (SE: 0.0682)  p=0.0707  ⚠️ FAIL\n",
      "\n",
      "Testing placebo treatment at t=2015 for cohort actually treated at t=2020.0\n",
      "  Coefficient: -0.1401 (SE: 0.1308)  p=0.2847  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2015.0          2013  0.123642  0.068170  0.070712        196        238   \n",
      "1  2020.0          2015 -0.140105  0.130758  0.284697         67        238   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "❌ Overall Result: 1/2 placebo tests failed. Suggests presence of pre-trends.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 3: Treatment defined by 'cc' *****\n",
      "***** (Among CCI sellers: Never certified vs Late certified) *****\n",
      "**************************************************\n",
      "  Filtered to CCI sellers only: 2949 households\n",
      "\n",
      "  Filtering to households present at baseline year 2011\n",
      "  Households with baseline data: 1252\n",
      "\n",
      "  Treatment assignment for cc:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2011\n",
      "  Total households in data: 1252\n",
      "  Control group: 277 households\n",
      "  Treatment group: 198 households\n",
      "  Excluded: 777 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 475\n",
      "  Total observations at baseline: 475\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 277 households\n",
      "    Treatment (1): 198 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 277\n",
      "  Treatment households: 198\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 321/475 (67.6%)\n",
      "  Mean SMD: 0.062\n",
      "  Max SMD: 0.137\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 419/475 (88.2%)\n",
      "  Mean SMD: 0.087\n",
      "  Max SMD: 0.247\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 451/475 (94.9%)\n",
      "  Mean SMD: 0.093\n",
      "  Max SMD: 0.250\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 329/475 (69.3%)\n",
      "  Mean SMD: 0.050\n",
      "  Max SMD: 0.101\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 409/475 (86.1%)\n",
      "  Mean SMD: 0.067\n",
      "  Max SMD: 0.141\n",
      "\n",
      "kernel:\n",
      "  Matched: 475/475 (100.0%)\n",
      "  Mean SMD: 0.102\n",
      "  Max SMD: 0.264\n",
      "\n",
      "radius:\n",
      "  Matched: 475/475 (100.0%)\n",
      "  Mean SMD: 0.102\n",
      "  Max SMD: 0.264\n",
      "\n",
      "stratification:\n",
      "  Matched: 475/475 (100.0%)\n",
      "  Mean SMD: 0.102\n",
      "  Max SMD: 0.264\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Tanzania: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Tanzania - CC (CCI sellers: never certified vs late certified)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1  All\n",
      "treatment_group               \n",
      "0.0              215    0  215\n",
      "1.0              245  294  539\n",
      "All              460  294  754\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "  Treatment=1: [np.int64(2011), np.int64(2013), np.int64(2015), np.int64(2020)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.238198, unique_values=2\n",
      "  treatment_group: variance=0.204108, unique_values=2\n",
      "  post: variance=0.238198, unique_values=2\n",
      "  emp: variance=0.225390, unique_values=2\n",
      "  vharvest: variance=11147523653632.000000, unique_values=526\n",
      "  lognf: variance=36.087852, unique_values=366\n",
      "  educh: variance=0.197618, unique_values=2\n",
      "  head_age: variance=211.360001, unique_values=71\n",
      "  head_sex: variance=0.165858, unique_values=2\n",
      "  dependency_ratio: variance=0.796575, unique_values=43\n",
      "  farmsize: variance=11.093071, unique_values=545\n",
      "  asset: variance=1.984573, unique_values=478\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 754\n",
      "  Unique households: 329\n",
      "  Unique time periods: 4\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 215 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 245 obs\n",
      "     Treatment=1, Post=1: 294 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 754/754 (100.0%)\n",
      "  Mean: 0.658, Std: 0.475\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0766\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -3.1679\n",
      "No. Observations:                 647   R-squared (Within):               0.0986\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.5815\n",
      "Time:                        15:47:45   Log-likelihood                   -101.33\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.8295\n",
      "Entities:                         329   P-value                           0.0033\n",
      "Avg Obs:                       1.9666   Distribution:                   F(9,307)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             13.755\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                   F(9,307)\n",
      "Avg Obs:                       215.67                                           \n",
      "Min Obs:                       118.00                                           \n",
      "Max Obs:                       329.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post          -0.0801     0.0996    -0.8036     0.4223     -0.2761      0.1160\n",
      "treatment_group      1.2974     0.4112     3.1551     0.0018      0.4883      2.1065\n",
      "vharvest          8.286e-09  1.035e-08     0.8005     0.4241  -1.208e-08   2.865e-08\n",
      "lognf               -0.0065     0.0059    -1.0960     0.2739     -0.0180      0.0051\n",
      "educh               -0.0340     0.1172    -0.2905     0.7716     -0.2646      0.1966\n",
      "head_age             0.0027     0.0036     0.7365     0.4620     -0.0044      0.0098\n",
      "head_sex            -0.2542     0.1187    -2.1425     0.0329     -0.4877     -0.0207\n",
      "dependency_ratio     0.0301     0.0482     0.6234     0.5335     -0.0649      0.1250\n",
      "farmsize            -0.0168     0.0120    -1.4073     0.1603     -0.0404      0.0067\n",
      "asset                0.0275     0.0313     0.8771     0.3811     -0.0341      0.0890\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.5598\n",
      "P-value: 0.0000\n",
      "Distribution: F(330,307)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Tanzania - cc ---\n",
      "Event times for treated units: [-9.0, -7.0, -5.0, -4.0, -2.0, 0.0, 2.0, 5.0, 7.0]\n",
      "Omitted period (reference): -2.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m9.0 + treat_event_m7.0 + treat_event_m5.0 + treat_event_m4.0 + treat_event_0.0 + treat_event_2.0 + treat_event_5.0 + treat_event_7.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0160\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0578\n",
      "No. Observations:                 754   R-squared (Within):              -0.0491\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.0611\n",
      "Time:                        15:47:45   Log-likelihood                   -225.23\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      0.8423\n",
      "Entities:                         329   P-value                           0.5658\n",
      "Avg Obs:                       2.2918   Distribution:                   F(8,414)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       4.0000   F-statistic (robust):             0.5007\n",
      "                                        P-value                           0.8557\n",
      "Time periods:                       4   Distribution:                   F(8,414)\n",
      "Avg Obs:                       188.50                                           \n",
      "Min Obs:                       105.00                                           \n",
      "Max Obs:                       329.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m9.0    -0.3811     0.2825    -1.3493     0.1780     -0.9364      0.1741\n",
      "treat_event_m7.0    -0.1417     0.3767    -0.3761     0.7070     -0.8822      0.5988\n",
      "treat_event_m5.0    -0.4252     0.3773    -1.1269     0.2604     -1.1668      0.3165\n",
      "treat_event_m4.0    -0.0559     0.1335    -0.4188     0.6756     -0.3183      0.2065\n",
      "treat_event_0.0     -0.0455     0.0991    -0.4588     0.6466     -0.2403      0.1494\n",
      "treat_event_2.0     -0.0229     0.2090    -0.1094     0.9130     -0.4337      0.3880\n",
      "treat_event_5.0      0.1858     0.2382     0.7803     0.4357     -0.2823      0.6540\n",
      "treat_event_7.0      0.0602     0.2572     0.2341     0.8150     -0.4454      0.5658\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.3037\n",
      "P-value: 0.0053\n",
      "Distribution: F(331,414)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Tanzania_cc.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q5s6d66aj0okI7UM0qv6skws6XgqYo7VRhbHUe6/2ZKzCnVs6NpruS8+bqomrt1zPi6pVK0jVc67nQ+Pac0MnAbwTKurJVQE2BaAZe0SVkaDeVGUL6CSNqp/r/zpuSqXfsWPHAe9LNDrOSgVXlXcdJwWpOrmhqco0TEGF4hRAK7Vej5/Xbr31VndcVYRMJ5lUkVxj87VOr4HIyt6alkrHSq9dvSZ1QkcnI/R+U9XyjJXHAaAooqcZAJDnFACosNT+Ll4vXiQvXVc/3mOl1CqYVoDo9bBqqiT1rKrnVIHAgU6ZpIBcaeAKOtXzplTirCioHTNmjJvW6IgjjnA9qNon9V4qWFZPp65XMB3JC5AVeGuaKgWNoiDu9ddfd+NK1Zun9FilDmusb+S8wB4FLwoUdV8K+PTYGq+s3ngvfTwW7/qCSs32qEdVz7OeOz1PaqPG0KoAmI6hnjcFskrlzqnI26iXXycf1Iua8fWnwFQBvAJCBYZ6zSnY1gkKvYY0Xn348OHufvSc5mVP8zvvvONOgCgw18kIvcaUhaB5yC+99NI8f8yMPf0qbqZiZHrt6fH1XHiPHzmEQNNjjRw50j0/OvGgbfX86r2mbb3pvgCgKEsIMRAFAIBiSQG2xvKqorR6r/OrwjgAAPGMnmYAAIoZVTxWz7ymWVKvoQpSETADABAdPc0AABQzGkOstGAVgdJ81pqrmSmDAACIjp5mAACKGRUAU8+yxjO//PLLBMwAAGSBnmYAAAAAAGKgpxkAAAAAgBgImgEAAOIISYIAULCSCvjxAADIM5p7995777Uvv/zSzX07efJku+iii6Jum5ycbBUrVnTzHWuO2V69eplfaH5lzQeteW8PO+wwt05zVGue6EmTJlmVKlVydb9NmzZ1cw/PnDkz5jaax1hzI2dXnTp13Fy9fnX55ZfbuHHj7M0333QFzw6U5snWfMo5oem76tata/nh008/da+Vhx56KLxO81rr/3o/qLAbACBvETQDAOLS2rVr7YEHHrCBAwe6gDlS1apVrWvXrunWpaam2urVq23ChAn2888/26+//mp33nmnFXf169e3Pn36pFu3YcMGmzhxYtTnMbcBfLzSSYeMz498/vnn7m+063Sb/DB16lS75ZZb7Jhjjkm3/rTTTrO33nrLnfx4/fXX8+WxAaA4I2gGAMSlxx57zPUeDxgwINN1jRo1sscffzzq7RQ0Dx482PVEKuBp3bq1FWcdOnRwl0jqsVfQnNXzWFzoJEG058ALmgvy+QkGg1HXqxL6TTfdZJdccomNHj3aV1kUAFAUMKYZABB35s2b54KDc845J8e9et26dQun2yqNFigKunTpYs2bN7ennnrKZVUAAPIOPc0AgLjz6quvumJI/fr1y9XtvfGmmzZtSrd++vTpLs11xowZtnHjRitRooQ1aNDApb9q3HEgEEg3Xvjwww93Y2iVJr5+/XrXM/v++++722ks7GuvveZ6ttesWeN6CTXe9Nhjj7UrrrjCKlWqlKt91349//zzLuBXirru56ijjrIhQ4a48cYFwRvnq556jRt++umnbfv27da+fXt3bGTHjh32yiuv2JgxY2zlypVWtmxZ69Spk1199dXuuYuk8dsK9DQ2fcSIEe6vnk+1R8dY2QRJSel/smi8t7ZVqv22bdvcWHD1tsYycuRIN2b8r7/+sj179li9evWsR48e7r61b3nt22+/tTfeeMPmzJljaWlpduihh9q5557r2qOe4UjTpk1z82XPnTvXvSarV6/u0uL12tJ+ym233WajRo1yyxqzreewb9++9uijj4bvR/f94IMP2jfffGMnn3xynrcJAIorepoBAHFl9+7dNnbsWGvSpEk4oMipRYsWub+RRZNUEOv88893AcfBBx/sArmGDRvaH3/8YQ8//LC7ZKRg8LrrrnMpvAoIa9eu7QJm9YQroHnnnXesdOnS1r17d2vbtq3b/j//+Y9deumlLpDKqWXLlrkiZkotVxCpsa0KsLTvWq+gqyD99ttvds8999ghhxzi0tx1gkEU+ClAfO6559zxUlCv677++ms766yzXKCbkYJmBbDvvvuuuz8F4wqMn3jiCRcIRvr7779dloHarZMGen4VyOvEhndsIym4vuOOO2zBggXWpk0bF5Bq3LbWX3bZZXlejfrJJ590JwdmzZplLVq0cL3AixcvdmPoM46jV6G3iy++2H766Sf3utNJFb2GPvzwQzv77LPdCRdp165deHx5zZo13QkLrYvkjXVWYTAAQB4KAQAQR37++edQkyZNQvfdd1+m63755Rd33QUXXBDz9p9//rnbpnnz5qG//vrLrdu5c2eoffv2obZt24bmzp2bbvtx48a57Vu3bh3au3dveL3W6XLrrbeG16Wlpbm/l156qbvugw8+SHdfy5cvDx1xxBHuumnTpoXXa3+1btasWeF1xx57rFu3YcOG8Lqzzz7brXv++edDwWAwvH7kyJFufc+ePUOpqanp9lFtyqn9PY/Lli0Lt3/48OGZ2n/DDTe46+69997Qnj17wtf/+OOPoVatWoU6d+4c2rp1a6a29ujRI7R06dLw+ilTpoSaNWsWatGiRWjbtm3h9QMGDHDbP/XUU+F1ehzvcXVRG7z1bdq0cY+5fv368Pbbt28P9enTx207ceLEHD9H3uNkNH78+PCxWLJkSXi9juMZZ5zhrvv000/D6y+66KJM+6Bje/vtt2dqo3dcBg8eHHO/unfv7o555GsVAHBgSM8GAMQVVRAW9TTH8ueff2ZK1VVKrnoa1UspN954o+vRFKUCH3300S6FVlNSRdJ6VedW5W31oNaoUSPd9eqd9ih9W72WSv9WMSb1qkZSuvGRRx7ppgZatWpVjtqtlHFV/O7YsaNL746kXmalAytlWz2WGasr56eM7dfzpPRq9ZqqV1XF2jzqcVYPtHrK9RxE3lbUOxuZPaC26jir517HrVWrVq73WT3V6rm+5pprwtuqd1Zp8kpdVmq4Z+vWrbZr1y7XI12hQoXweqVka7oy3a/2Na941avvu+8+V5nco2wE9ZiffvrpLm371FNPdeuVYu/1HnuUvq22tWzZ0vWM54Serx9//NGlhef0tgCA6AiaAQBxRUGZKBU6FqXeetWNPaVKlXJTKGmsp1J7lTLrUaCmNOCM6cJLliyx33//3QXckpKSkumxMo7PVcCj4C2SAmml2c6ePTsctEe7r6xobl6JNfewgnEFzdquoIJmpYZnnIJKJzU0flvjvSMD5sj9VNCs/cwYNEcL8qpVq+b+KvD1xv+KUpUjx5hLuXLl3PMTOY+0jrkCbD3vSnfW+HSdCNEJE43B1iWvKOVe4+KVOh/tflWoS/ujgFaBvQJ3VS7XOGvNL64xyTp2eh40dCDj85Md3rh2vU8ImgEgbxA0AwDiigpheQFSLBpfrIJeOaHAVkGnxoPOnz/fjT/2qhB7hZsyjn1VIK4ezmg0FlrjczWudenSpW5sb1b3ld2TBRqHq0ss3hjYghDZc5txP1W0yitcld39LF++fKZ1XgEwb7qldevWZeqZjZSxGJqeb40xVqE0Bau6PPLII+FCYBdccEGWJ2ByYvPmzeHjrF7xrKgdCpqVEaGAXicRXnjhBXdRr7jGNqtXXmPhc8J7DnXiCACQNwiaAQBxxQtkY81Zm9seQqU8K621ZMmSrhKzpqZSL7IC8GuvvTZqgamMPZ0eBT4K1BSwKd1bvdtK/Vbhpk8//dRV2M4pr73qwc2qSvb+grW8FK393n6q3Y0bN45529xW+s5YeTqjjFW2RcW4vvrqK3d81Qut4lsqqqZK3zqxoeJsGYtq5YZX3E3BsArJZcU72VKxYkV3gmfmzJmuCJ2qreukjXfSQQXMVCgsp/uQm0JzAIDoCJoBAHFFQUa06aIOhMbXKqBS4KTpnCpXrpzueo2LzS4FY5qCSWm4mnIq4xhpBWm5TYWWE044wQYOHGh+5e2n0pNVWTuveWPKlQkQjdcTHS1I1XOniyxcuNAdJwWq6rn3pso6EOohVkq6sggef/zxHN1Wrz0vcFcb9DpR9XHNu9y/f/+oqe7ReO8L730CADhwTDkFAIgrXnGlWMFRbqdOEo11zhgwK7jyijVlJ6VaY6DV26pxsxkD5r1794bH5Oa0p9wbI6tCX9EoSNM0VxnHchc0bz/Vmxtt3Pbbb7/tpktS725uaMyyepvHjx+f6f71/OpxMx7bU045JVMAr17wW265xS3ntChbLArMlaWwc+fO8HHOmJLes2dPN82V9lXjtFUszisKFnniQVOZqQCd7ss7abO/XnbxXqve9F8AgANH0AwAiCveGE8v0M0LCk5EgVhkMKuxyEOHDg3/3ysIlp37UtC0ffv28HotK0jzxvxm574iqXCZAj0FheoNj9xP9ZKrarOqTCtoK0yqRK15k1Xc6qGHHnLBYeQ4b/Xuqop5xhMKOelpVuCp51HVqCPTkfX/jGN5GzVq5OZwVjaBxpdH+uKLL9zfvHzONFe03HXXXW5uZo8CZFUT1/hlnZhRgK05vJVOrnRszemdsaCaAmClsStrQTR0QLZt2xb1sXVSRydtNNY+t88vACAz0rMBAHFFPY1lypSJ2pOXW5oGSKnUmipp7ty5biyzgi+NM9W4XfVuK4BW77bGJmdFFYsV2Gt6qBNPPNGl3Cpw1JRRqpis22t8tKa5ygn1MqrC9yWXXOJSdj/88ENXjdnbT2+aIz/0MCp4VYGt9957z02FpXHWarsqSyu4VU9rZPXynFJAqhME//3vf91JBD0POm6ajkrTNCk496hg3O233+6eG2USaEy4glAFtAretRw5ddWBUq/25MmT3b6pB7l169YuVVrHSEXsdPw1TjmyLUq/VsV13UZVvbWdnivveo+mMktMTHTXDRo0yFXejpx+TFkRW7ZsseOPPz5mgToAQM7R0wwAiCsKmE866SQXIClwygvqHVZPn4INBXcqFqWxyXqcjz76yC699FK33Q8//LDf+1JQo0Jgmi5I+6peYPX+KXjSGNXhw4dn+74yUjCv6t7eVETqGdfzoJ5dTeN03nnnmR+osrWet8GDB7uiWCpupRMFSt3W+OGMc2jnlNKXFZCrV1cnJPRcqpL3Sy+9FLXatJ4XFWbT4+s1o+Or46zq1Cq2pWA0L91///02bNgwty96vIkTJ7rgXPNQKzDW2GePgnylrOsEi06AqIK75hnXa1EF4yILiuk+dN+q9v3LL7+4+42k8dmiqasAAHknIZTTOS8AAChkSv3t1auXC4byo9gUEG+Urq8iZzpJoVT07Ix/BgBkDz3NAIC407BhQxc0KziIHDcMFFde1sFVV11FwAwAeYygGQAQl26++WaXCv3iiy8W9q4Ahd7LrPTzI4880s0JDgDIWwTNAIC4pHGzSs1+44038mzKICAeaVy23gMPP/xwYe8KABRJjGkGAAAAACAGepoBAAAAAIiBoBkAAAAAgBgImgEAAAAAiIGgGQAAAACAGJJiXYGCp5pswaB/67Jp2sd4LxtHG/yBNvgDbfAH2uAPtME/ikI7aIM/0AZ/SPBpGwKBhGzPa0/Q7CMKmDdu3GF+pNdTcnKipaSk+fJFnx20wR9ogz/QBn+gDf5AG/yjKLSDNvgDbfCHBB+3oUqVspaYmL2gmfRsAAAAAABioKcZAAD4Vmpa0H75Y41bPqJlTUtK5Hw/AKBgETQDAABfW79lV2HvAgCgGCNoBgAAvqVCLR2b1QgvAwBQ0AiaAQCAbwUSEqxO9XKFvRsAgGKMgUEAAAAAAMRATzMAAPCtYChkm7buccuVK5R0Pc8AABQkepoBAIBvBYMh+2nWSnfRMgAABY2eZgAA4GtlSycX9i4AAIoxgmYAAOBbmpf5xA71Cns3AADFGOnZAAAAAADEQNAMAAAAAEAMpGcDAADfSgsGbcrctW65U/MalhjgfD8AoGARNAMAAN8KhczWbNwZXgYAoKARNAMAAN/SvMyHN6keXgYAoKARNAMAAN8KBBKsfs3yhb0bAIBijIFBAAAAAADEQE8zAADwrWAoZNt27HXL5cuWIEUbQDpjpyy1r6cuy/HtenSsZz071c+XfULRQ9AMAAB8KxgM2Q8zV7jl3l0bWCCRoBnAP3btSbVN2/bk6nZAdhE0AwAAXytVgp8rAKIrXTLJKpcvmW5dKBSyzdv3ZahUKlfCEqJkqOh2QHbxagEAAL6VlBiwkzqTQgkgOqVYZ0yz3rM3za4cNt4tP3p5FyuRnFhIe4eigkJgAAAAAADEQNAMAAAAAEAMpGcDAADfSgsGbfr8dW65fdPqlhjgfD8AoGDxzQMAAHwrFDJbuX6Hu2gZAICCRk8zAADwLc3L3LpRtfAyAAAFjaAZAAD4ViCQYA1rVyjs3QAAFGOkZwMAAAAAEAM9zQAAwLdCoZDt2J3qlsuWSrIEUrQBAAWMoBnwobFTltrXU5fl+HY9Otaznp3q58s+AUBhSAuG7Ntp+z4Pe3dtYEmJBM0AgIJF0Az40K49qbZp255c3Q4AiprkJEaTAQAKD0Ez4EOlSyZZ5fIlM6Uobt6+1y1XKlciaoqibgcARUlSYsB6dWlQ2LsBACjG+IUN+JBSrDOmWe/Zm2ZXDhvvlh+9vIuVSE4spL0DAAAAig/ynQAAAAAAiIGeZgAA4FtpwaD9tmiDW25zaFVLDHC+HwBQsPjmAQAAvhUKmS1ds81dtAwAQEGjpxkAAPhWICHBWh5SJbwMAEBBI2gGAAC+FQgkWOO6lQp7NwAAWRg7Zal9PXWZ5VSPjvUyFb/1I4JmAAAAAECu7dqTapu27cnV7eIBQTMAAPAtzVG/e2+aWy5VIjHqHPUAgMJVumSSVS5fMtPn9+bte91ypXIlon5+63bxID72EgAAFEtpwZBL+5PeXRtYUiJBMwD4jVKsM6ZZ79mbZlcOG++WH728i5VITrR4RdAMAAB8jQJgAIDCRNAMAAB8KykxYKceeUhh7wYAoBhjnmYAAAAAAGIgaAYAAAAAoKinZ6emptrbb79t77//vq1YscJq1Khhffv2tcGDB1tycnKWt01LS7N27drZnj3Ry6R//fXXdvDBB4f/v2rVKhs2bJhNmjTJduzYYc2bN7chQ4ZY165d87xdAAAUZ2nBoM3+a6NbbtWwiiUGON8PAChYRSZofuCBB1zA3LFjRzv++ONtxowZNnz4cJs/f777m5W///7bBczdunVzwXNGFStWDC+vX7/e+vfv7/726dPHypUrZ6NHj7YBAwbYs88+6x4bAADkjVDIbPGqrW655SFVCnt3AADFUJEImhUgK2Du1auX6wH25gW77bbb7JNPPrHx48fb0UcfHfP2Cqzl3HPPtR49emT5WE8//bStXLnSXn75ZevevbtbN3DgQOvXr58L3I866igrUaJEnrYPAIDiXDm7Wf3K4WUAAApakchxeuedd9zfq6++OrxOk2cPHTrU/R05cmSWt/eC5saNG2e5nVKxFYS3adMmHDBLzZo17cILL7TVq1fbhAkTDrA1AADAEwgkWLODK7uLlgEAKGhFImieNm2aVa9e3Ro1apRuvYLZBg0a2JQpU/YbNJcqVSrduOVoZs2aZXv37rXOnTtnus5bt7/HAgAAAADEj7gPmhXEqoe3Xr16Ua+vU6eObdq0ybZs2RLzPhYsWGAHHXSQPfPMM9azZ0877LDD7OSTT7Y333zTpXl7li5d6v7Wr18/6uN446MBAEDe0PdwSmqau0R+JwMAUFDifkzz5s2b3d8KFSpEvb58+fLu77Zt29IV9PJs377djVHWF/Gnn37qCnmpKNi4cePsoYcesnnz5tnDDz+c7rG8+4z2OLq/A+HX4Vrefvl1/4pDGyL3W8vx3o543X+hDf5AG4pHG9KCIRvzyxK33LtrA0vKhxRtjoN/FIV20IbCx28mf0goIsehSATNmmpKYhXf8tbHmk5q3bp1biyzeqpVRExp2rJ161a79NJL3Xho9T6rkFhKSkrMx9rf42SHXkjJyYnmR9q3xMRE9zdeT/THexvSIvY5KTnRt6+Von4chDb4A20oHm1ICCRY4H/TTOlzLykx75PkOA7+URTaQRsKH7+Z/CHN58chJ0F83AfNXpDrBbTR0relTJkyUa8/5JBD7PPPP8+0Xj3XN998s1188cX2xRdfuKA5q8fyHqd06dK5boveDCkpaeZH3ps11aXHWVyK9zakRrw2tByvYyvi/TgIbfAH2lA82qBMsN5d9tUcCaYFLSWY9w/CcfCPotAO2lD4+M3kD6k+Pw45eU7jPmjWPMk6A63062i89dFSqvenZcuW7u/y5cvdXy+9O9pjHcjjRPL7G0L75/d9LKptiNzneG1DJNrgD7TBH2hDVhLS9Qbk5/PEcfCPotAO2lB4+M3kD6EidBz8FvDnmNKia9euHQ5sM9J6VdZWcB3NmjVrXMXrDRs2ZLpu9+7d7m/JkiXdX1Xi9u4z2uN4PdcAAAAAgKIh7oNmad++vaugvWzZskwBsapZt23bNuZtP/vsMzfH8kcffZTpuunTp7u/rVq1Cvc8K0V76tSpmbb1pprK6rEAAEDOBIMhm714g7toGQCAglYkgubTTz/d/VUhL286Cv3V/+Xss8+OedsTTzzRDbDX9FIqCuZZu3atPfnkk5acnBy+vcZFa3vNC63q2pHB+VtvvWW1atWy7t2751s7AQAoboKhkC1avsVdtAwAQEGL+zHN0rVrVzvllFNszJgxbvqoTp06uV5iXXr16hUOZFUR+4033nDjji+55JJwyvVVV13l5mju06ePnXTSSa4i93fffWcbN260+++/P928zEOHDrUJEybYkCFDrHfv3q5g2OjRo91c0M8995wLsgEAQN4IJCTYoXUrhpcBAChoRSJolscee8waNWpko0aNstdff92Nc77++utt4MCB4W0UNI8YMcLq1KkTDppFAXDDhg3d7XR7FRZTSvbgwYPtqKOOSvc4ut/333/fHn/8cRdYp6WlWfPmzd3/u3TpUqBtBgCgqAsEEqzVIVULezcAAMVYkQmaVRBMwa8usdStW9fmz58f9Tr1VOuSHep5Hj58eK73FQAAAAAQH4pM0AwAAIoe1SjxhjIrOzuBFG0AQAEjaAYAAL6VFgzZFxP/dsu9uzawpESCZgBAwSoS1bMBAAAAAMgP9DQDAADfSgwkWK8uB4eXAQAoaATNAADAtzSGOTkpsbB3AwBQjJGeDQAAAABADPQ0AwAA3woGQ7Zg2Wa33KReJTdvMwAABYmgGQAA+FYwFLJ5Sze55UPrVrSAETQDAAoWQTMAAPAtTct8SK0K4WUAAAoaQTMAAPCtxEDA2hxarbB3AwBQjFEIDAAAAACAGAiaAQAAAACIgfRsAADgW6lpQRszaYlbPqXLwZaUyPl+AEDBImgGAAC+r6ANAEBhIWgGAAC+lRhIsJ6d6oeXAQAoaATNAADAtxISEqx0SX6uFDVjpyy1r6cuy/HtenSsFz6JAgAFhW8hAAAAFKhde1Jt07Y9ubodABQ0gmYAAOBbwWDI/ly5xS03ql3RAqRoFwnKHqhcvmS6daFQyDZv3+uWK5Ur4bIMot0OAAoanzwAAMDXRcD+WLzRLR9Sq4IFjKC5KFCKdcY06z170+zKYePd8qOXd7ESyYmFtHcAkB5BMwAA8C11NtavWT68DABAQSNoBgAAvpUYCNjhTaoX9m4AAIqxQGHvAAAAAAAAfkXQDAAAAABADKRnAwAA30pNC7o5fUWFo5ISOd8PAChYBM0AAMDXUlKDhb0LAIBijKAZAAD4VmIgwU7oUC+8DABAQSNoBgAAvpWQkGDlSicX9m4AAIoxBgYBAAAAABADPc0AAMC3gsGQ/b16m1tucFB5C5CiDQAoYATNAADAt4KhkM36c71brl+znAWMoBkAULAImgEAgG8lJJjVrlY2vAwAQEEjaAaAGDQ37NdTl+X4dj061nPzyQI4cImBgHVqXrOwdwMAUIwRNANADLv2pNqmbXtydTsAAAAUDQTNABBD6ZJJVrl8yXTrQqGQbd6+1y1XKlfCTYcT7XYAAAAoGvhlBwAxKMU6Y5r1nr1pduWw8W750cu7WInkxELaO6B4SE0L2rfTlrvlEzrUtaREZssEABQsgmYAAOBru/cy5AEAUHgImgEAgG9pXuZj29UJLwMAUNAImgEAgG8FEhKsYrn0tQUAAChIDAwCAAAAACAGepoBAIBvBYMhW75uu1uuW70cKdoAgAJXZILm1NRUe/vtt+3999+3FStWWI0aNaxv3742ePBgS05O3u/tx40bZ6+++qrNnj3b0tLS7JBDDrHzzjvPzj333HTb6bp27drZnj3R5279+uuv7eCDD86zdgEAUJwFQyGbsWCdW65drawFjKAZAFCwikzQ/MADD7iAuWPHjnb88cfbjBkzbPjw4TZ//nz3Nyv//e9/7d5777XKlStbr169rGTJkvbDDz+4dYsWLbK77rorvO3ff//tAuZu3bq54DmjihUr5kv7AAAojjQVes0qZcLLAAAUtCIRNCtAVsCsgHfYsGFuXSgUsttuu80++eQTGz9+vB199NFRb7t161Z77LHHrHr16jZq1Cj3V4YOHWrnn3++vfXWW9avXz9r3ry5W68gXNQD3aNHjwJrIwAAxVFiIGBdWh5U2LsBACjGikQhsHfeecf9vfrqq8PrEhISXOCrvyNHjox5259//tl27txp/fv3DwfMUrZsWbv00kvd8k8//RRe7wXNjRs3zpe2AAAAAAD8o0j0NE+bNs0FvI0aNUq3vmbNmtagQQObMmVKzNs2bNjQbrjhBuvevXum65SmLQqqI4PmUqVKMW4ZAAAAAIqBuA+a9+7da6tXr7bDDz886vV16tSxxYsX25YtW6KON27WrJm7RPPtt99m6lVesGCBHXTQQfbMM8/YmDFjbOXKlVa3bl1XNOzCCy90PdsAACBvpKYF7YeZK9zyse3qWFJikUiSAwDEkbgPmjdv3uz+VqhQIer15cuXd3+3bduWoyJd6r0ePXq0VatWzY477ji3bvv27S5I1njpTz/91BUcU1EwVd5+6KGHbN68efbwww8fUHv8GnN7++XX/SsObYjcby3Hezviff+95XhvR7zuv9CG4tEG3e/O3Snh5fx4HI6DP/D56h/x3gZeS/6QUESOQ5EImjXVlJQoUSLq9d76WFNERbNw4UI3PjoYDNr9999vpUuXduvXrVvnep3r1avnCo4pTdsrJqbxzxo73bNnz5hFx/ZHL6Tk5ETzI+1bYmKi+xsKWVyK9zakRexzUnKib18r+8Nx8Id4Pw5CG4pHGxKTAnZs+7puuWTJJAvkw68ujoM/8PnqH/HeBl5L/pDm8+OQk6+TuA+avcA1JWXfWeho6dtSpsy+6Sr2548//rBBgwa5HmyNdT7hhBPC12nu5s8//zzTbdTLffPNN9vFF19sX3zxRa6DZr0ZUlLSzI+8N2tqalpcvmmLQhtSI14bWo7XBEWOgz/E+3EQ2lB82lCxzL4T4GmpQcuPb0mOgz/w+eof8d4GXkv+kOrz45CT5zTug+Zy5cpZIBBw6dfReOu9NO2sTJw40YYMGWI7duywa6+91q644ops70fLli3d3+XLl9uB8PsbQvvn930sqm2I3Od4bUOkeG0Dx8F/aIM/0AZ/iOc28PnqP/HaBl5L/hAqQsfBbwF/jin9unbt2jGDVa1XZW0F11kZO3asDR482FXKvvPOO9NNX+VZs2aNq8S9YcOGTNft3r07XcVtAABw4IKhkK1Yt91dtAwAQEGL+6BZ2rdv7ypoL1u2LFOQ+/fff1vbtm3328N84403ujHMjz32mF100UVRt/vss89cheyPPvoo03XTp093f1u1anVAbQEAAP8IBkM2dd5ad9EyAAAFrUgEzaeffrr7q+Jcqmwt+qv/y9lnnx3zthq7fNNNN7kx0Y888oiddtppMbc98cQT3WD8N9980xUF86xdu9aefPJJS05OzvKxAABAzlWrWNpdAAAoDHE/plm6du1qp5xySnje5E6dOrmeX1169epl3bt3D1e5fuONN9z45ksuucSte/vtt126dY0aNWzp0qVu/uWM2rVrZ0ceeaQ1aNDArrrqKrdNnz597KSTTnLVu7/77jvbuHGjq7Rdv379Am8/AABFleZlPrJ1rcLeDQBAMVYkgmZRWnWjRo1s1KhR9vrrr7txztdff70NHDgwvI2C5hEjRlidOnXCQbPmY/Z6i3VdNAMGDHBBs6hQWMOGDd1j6LFUhEwp2RoPfdRRRxVIWwEAAAAABaPIBM0qCKaAVpdY6tata/Pnz0+3TsFvTqlXWxcAAAAAQNFWZIJmAEBmY6csta+npi+SmB09Otaznp0YboLCl5oWtJ9mrXLLR7Wu5dK1ASCWlNQ0mzxnTfj/L372h7VvWt06NqthyUmJhbpviF8EzQBQhO3ak2qbtu3J1e0Av9iyPeevYQDFz8yF6+zV0XNt5+5/vsN+XbTeZi5cb+9+u9AG9WphbRtXK9R9RHwiaAaAIqx0ySSrXD79/PGaXWDz9r1uuVK5EpaQkBD1doAfBAIJ1rXVQeFlAIgVMI8Y+btlnJjOm95dgfQzI2fZkH6HWbvG1QtjFxHH+FUEAEWYUqwzplnv2ZtmVw4b75YfvbyLlUgmXQ3+FUhIsBqVyxT2bgDweUq2epj3N5O7rtd2Tw6pQqo2coSBQQAAAADi1tR5a9OlZGdF202bty7f9wlFC0EzAADwrWAoZKs37nQXLQNARjMXrLcoI42i0nYzFhA0I2cImgEAgG8FgyH75Y/V7qJlAMho++6U8Njl/dF22h7ICcY0Ix2mpwEA+E2lDMXsACBSuVLJrgc5O4GzttP2QE4QNCMdpqcBAPiJ5mU+pm2dwt4NAD7Wrkk1m57NlGsF1oc3oXo2coagGekwPQ0AAADiScdmNdw8zNkpBlamVJJ1aEbQjJwh0kE6TE8DAACAeKLpowb1auHmYc4qQ1vdPtqO6aaQUxQCAwAAvpWaFrQff1vpLloGgGjaNq5mQ/od5nqSI3kJklp/Tb/Wbjsgp+hpBgAAvrZx6+7C3gUAcaBd4+r25JAqNmn2Gnv9q3luXdtDq1mHpjVcSjY9zMgtgmYAAOBbgUCCdW5RM7x8IJghAij6FBjrM8MLmi8/tSVDC3HACJoBAIBvBRISrFbVsnlyX8wQAQDIDYJmAABQLDBDBAAgN/gWAAAAvhUMhWzjln1jmqtULOV6nnOLGSIAALlB9WwAAOBbwWDIfv59lbtoGQCAgkZPMwAA8LXyZUoU9i4AAIoxgmYAAOBbSYkBO7593cLeDQBAMUZ6NgAAAAAAMRA0AwAAAAAQA+nZAADAt1LTgjZ5zhq33LlFTZeuDQBAQSJoBgAAvrZu867C3gUAQDFG0AwAAHwrEEiw9k1rhJcBAChoBM0AAMC3AgkJVq9GucLeDQBAMcbAIAAAAAAAYqCnGQAA+FYwFLIt2/e65YrlSrieZwAAChI9zQAAwLeCwZCN/3WFu2gZAOB/Kalp4ZkP5MXP/rCJs1e59fGInmYAAOBrZUolF/YuAACyaebCdfbq6Lm2c3dqeN2vi9bbzIXr7d1vF9qgXi2sbeNqFk/oaQYAAL6leZl7dKznLszRDAD+D5hHjPw9XcAsof8lCmn9MyNnue3iCd8+AAAAAIADkpKa5nqY9zeQRtdru3hK1SZoBgAAAAAckKnz1mbqYY5F202bFz+9zQTNAADAt9KCQVdMRhctAwD8aeaC9ZbdCQ603YwF8RM0UwgMAAD4lsbBrdqw43/L1Qt7dwAAMWzfnRIeu7w/2k7bxwuCZgAA4Fual9mrssoczQDgX+VKJbse5OwEztpO28cL0rMBAIBvBQIJ1uCgCu6iZQCAP7VrUi1HPc2HN4mf7CGCZgAAAADAAenYrIaVKZW9RGZt16EZQTMAAMABC4VCtnXHXnfRMgDAn5KTEm1Qrxa2v5wgXa/ttH2xGdOckpJis2fPtrVr11piYqIddNBB1rJlS0tg3BEAADhAacGQfT9juVvu3bWBJSXy+wIA/Kpt42o2pN9hbh7myOmnvLHO6mFWwOzVqijyQfPmzZvtmWeesVGjRtmuXbvSXVe5cmU7//zz7bLLLrMSJUpYQUhNTbW3337b3n//fVuxYoXVqFHD+vbta4MHD7bk5P0PMl+1apUNGzbMJk2aZDt27LDmzZvbkCFDrGvXrlHbPnz4cPvhhx9s48aN1qhRIxs0aJCdcsop+dQ6AACKr5LJ8dMbAQDFXbvG1e3JIVVs0uw19vpX89y6todWsw5Na7iU7HjqYT6goFlB4wUXXGB//vmnlSpVyo488kirVauWBYNBW7lypU2fPt1GjBhhU6ZMsVdeeSVbQeuBeuCBB1zA3LFjRzv++ONtxowZLrCdP3+++5uV9evXW//+/d3fPn36WLly5Wz06NE2YMAAe/bZZ939eXbu3OnWz507104++WSrWbOmffPNN3bDDTe4AFrPCwAAWUlJTbOp89bazIXrbcfuVCtbKsnaNa7mxoPF44+J/JSUGLCTjzi4sHcDAJAD+i7r3KJmOGi+/NSWViKOT4DmKmh+/vnnbdGiRdazZ0978MEHrXz58pmC6ttuu83Gjx9vb7zxhuuFzU8KkBUw9+rVy/UWi8Y9aR8++eQTtx9HH310zNs//fTTLth/+eWXrXv37m7dwIEDrV+/fi4YP+qoo8I95m+++ab98ccfdv/999u5557r1l199dVu+YknnnC9zVWqVMnX9gIA4tfMhevCaWteupr+Tp+/zt79dmFcpq0BAFCU5aoQmHpW69SpY48//nimgFkqVarkAlGlSH/88ceW3955551w8OrRmOqhQ4e6vyNHjox5W6ViK7Bu06ZNOGAW9SBfeOGFtnr1apswYUJ4/bvvvuvadfbZZ4fXqWf6iiuucL3QY8aMyYcWAgCKSsA8YuTv4XFeXl0r76/WPzNyltsOAADEcdC8YcMGa9WqVZZp1yVLlrS2bdu6Htz8Nm3aNKtevbobWxxJgW+DBg1cmngss2bNsr1791rnzp0zXeet826/dOlSW7NmjUsBDwQCWW4LAEDGlGz1MO+v/rOu13baHioEFrRp89a6i5YBAIiL9OyGDRvavHnz3BjmjMFjpCVLlli9evUsPyngVW/w4YcfHvV69YgvXrzYtmzZYhUrVsx0vQJhqV+/ftTbyt9//51u22htUtCuEwXetrmhnobUtOg/CJS6lxjxXMfaLnIMWF5tG+v2BbkPsbbVD6isZiDJuG1CWoK7/2i3SQwkhKu+7+9+c7ttMBiyYBYbZ7Vt5POi5aSkgAWyeb+BQEL+bxsKue1jbpuQ4Lb3to11HKJtm5P7ze62GsKRlottMx4HbRP5/tzf/eZ224yPfSDbpmW4zg/v5dxsqzYnBDK/pw/kMyI/3vfetr/MWZOukmhWtN3kOWvsiJYHxbxfX7zvgyHTv2jHIdO2uXwv636Xrt3mlls1rGKhxLz9jIh87UVel9vPiGgK8jMi1mdrYf6OyO62kf93n1MR4x/98jsiJ+/7rL7nCuN3RE7e9xk/X/34OyKrbSOv835z+OV3RE7e99G+5/zwOyK777mcxBWF8dmTk1kMcxU0X3vttXbVVVfZfffdZ3fffXfUHueXXnrJFeFSCnd+0vhpqVChQtTrvfTxbdu2RQ2avdtHSzP31m3fvj1bj6U0bW/b3Ni5J8VGT4oedNesXMa6tPrnx9NXk5fGPONerWJpO7J1rfD/v5m2zPamRO+xqFSupB3Tbt/JAflhxgq3H5FS0zJ8iP5vto8ff1tp23bujXq/ZUomW49O/5xcmPD7Ktu8fU/UbVUU4JSIIi/6obh+S/qK7B69mft0axD+/9S5a23Npp0Wy+lHNQwvz5i3zlZt3OVO9kTjpjL534fbrD832NI1+36kRXNy54OtZIl9X+Z/LN5oi1dtjbltj471rEypfe+RuUs22qIVW2Jue9zhda1C2X3j5xcu32zzlm6Kehx0/I9rX9cqly/p/v/Xqi1uP2I58rBaVq1Sabe8ZM02m/Xn+pjbHtHiIDuoahm3vGL9dpuxIHaaqIoW1alezi2vXr/DFTaK5fAm1a1+zfLu9bNm4077+bfYWSitG1WzhrX3vc82bdltP/++Kua2LQ+pYo3rVnLLW7fvtfG/roi5bbP6la3ZwZXd8radKeFpbKI5tE5Fa9WwqlvetSfVvp66LOpxSExMsENqVbA2h+4bg7o3JWhfTl4S8371HOi5EH3RxXrPS+1qZa1T85rh/2e1bU4+IyqW3fe6ER2PA/2M8JQvU8KOb183/P+C+IzQidvI9/QBfUYsWGcr1++Iue2BfEa8991CW7Mx+udaLK+NmWejf1liR7Soma3PiIyOblunwD4jMh6HA/mMkHUbd9kvc1aHfxRu+d9r46vJS1xAkJefEZHvaX0+H9ao6gF9RkRTEJ8Rei9/+tPimN9xhfk7IjufEdqXZWv/ef89/M4MO6VzfevYfF9xPF/8jsjmZ4SOxcwF623xyi2++h2R08+IyPe1n35HZPyMiPV979m0bY9NmbvGN78jcvoZkfHz1Q+/I6pl8zMiWgzhp98RZ/doZhWS/vlNlOdBs+Zk1vjfDz/80H7++Wc77rjjXE+t5mnWdT/99JOrLq306JkzZ7pLpLvuusvycqopiTW1lbd+z549MeeZjnX7jLfNzmPFepzsSLCEmD33iUkBS44446of6qEY2fWJiRm2DQQsEAhlb9vEzPsQCP3zhklKTgxvr9vG3N/EhAz3m8W2gYz7kIP7VW9rFtkOmbfVl1kg6pklbeudhcpqH3LyPGTcNikpMfv3m6FtkcchkJjgeprD95uY/ft1PdRZbhvI/v4mJeZ4W3cmMzHRPW+xzvCla1vyfu43MfttS8zJ/Ua2zfUoB6IeB62PfB/p+yHLfYjYVmePs7ute7xsti07nxHhdiYnHvBnROxt8/czYt8XcPr39AF9RuznvXwgnxGpqblLK9btIh8nq8+ITPtQQJ8Ral/G43AgnxHeY0RuW61SmXz7jIh8T+fFZ0Q0BfEZofdDVt9xhfk7Iva2+47Hqg07XCG8lIj3yYp1O+zlL+baO98utCtOa+WP3xHZ/IzY9z0XyPp7rhB+R+TkMyLj56uffkd4j5H1d+I/x22/+1vAvyNy8hkR/XvOH78jkrPxGREthvDV7wj7X09gNiSEdAo3h5o1a+YOYC5u6m6ngDqvaJqnLl262LHHHmsvvPBCpuuvv/56+/LLL23cuHFuWqyMNCXW//3f/7lpqVQNPNLu3btdgbAjjjjCVQHX/ej+br31VjftVEaa07lMmTL27bff5vrH0foN0XsuCjOtas/eNBvy1E9u+cWbjg6Xi/dDekVO0qqCoaAL1lJT0+IyPTvyODx7w1FWqmRSXKZnux92iQHbuzc1LtOzMx4HvR/iLT177940uzriPe21Mx7Ts/WjJON72o/p2cNHzrJZi2L39MbS+tAqds0ZraPer5/Ss6Mdh0zb5tN7+UC3jXxPPz+0u/tsjcf0bPc0JyTE/I7za3r2rwvX23OjZmc51l9Nu6pvqyyryvspPdtLqU1JiX4sIrfNyf0WdHp25PvaT78jsrNtSkrQrnryR7f8/I3d0x3HeEvPzvj56offEdl9z0WLIfzy2SPVqpZ3J0ryraf5kUceMb9QSrTOFCj9OhpvfbT0a/FStqPdPuNts9pWlJqt3vXcyvhizSjyMyer7fJ628SIM0faNnymqwD3Ida2gYTAvm/TbG6rN0xIP/JifL546/d3v7ndVl9kiV5+ew63jTwOif87O5YX95tn21qC+7LOzrb6IlEbsvqRkJv7zcm2lsttMx6Hf764Dux+C/J9FHk2WLfxw3s5N9u6HxNR3tMH8hmRH+97bXv9mW1t4uxV9soX2T9pfFnvFulS5TLery/e9/pRmJCQ5Wfrgb6X9aNQqY1SumRSOCDIq8+IyPf0vg6B8P/y/fMkb7fVvKjZ+47zy3tZwcl/vpyXreJ42u7JRt1izmNeUL8jsvu+T1JKbSD2sSiM3xE52Tba56uffkfsb9vUhIj3tds24JvfETnZdv/fc4XzOyK720aLIfzw2ZPuZGM25Spo7tu3r/mFUqJr165ty5dHH1Og9SrSpeA6GlXX9raLdls55JBD9rut0tKVmu1tCwBAxrF7moc5O8XAypRKsg7N9o1ZK+7Ui+KNBXRjRhNz8CsHvqbxqzkpjjdt3rqoJ5IAIL9lrz/a59q3b+8qaC9bln6AvaaHUjVrTX0VS8uWLa1UqVI2derUTNd500d5t1dwrsv06dMzpaZn3BYAgEjqIRvUq8V+R1Dpem0Xq0etOPLGh+Y1TeulgjCeFz/7w2UEMN1XwVCxrOz29Gi7rApKAUB+ylVPs0yaNMk++ugjN61UVsWvlJbx2WefWX46/fTT7dNPP7Vhw4a5izfeWsty9tlnx7ytxiCfeOKJ9vnnn7txz8ccc0w44H7rrbfcOGgVPfOceuqpbuz0u+++a+eff344LVvrSpcubX369MnXtgIA4pfGZA7pd5ibh1k9ZwoEdA7W+6seZgXMWY3dLG6Umtin6z/VjvPKzIXrwsfB8+ui9TZz4XqXEcBxyH/bd6dke8oXbaftASBugubvvvvOhgwZkq1CYN7Yo/ykAlynnHKKjRkzxlauXGmdOnVyvcG69OrVKxz0bt261RX00hjlSy65JHz7oUOH2oQJE1ybevfu7aaUGj16tG3atMmee+65dFNqXXbZZfbVV1/Zv/71L5s8ebLVrVvXvv76a9fLrSm4Klf+p8Q9AAAZtWtc3Z4cUsWlms5YuM4FbQqWD29c3aVk08Oc/xQwjxj5e6axtN7PGh2TZ0bOcic4dLyQP8qVSg6fMNofbaftASBuguZnn33WBcxXXHGFqzitILQgguOsPPbYY9aoUSMbNWqUvf766y6NWpWuBw4cGN5GQfOIESOsTp066YJmbfv++++7OaV1QiAtLc2aN2/u/q/K3JE0Nvqdd95xvdg//PCDm15L45ifeuopO/nkkwu0zQCQF+mo7ZtWd+NtCdYKjp5rjc3sethBbhqMrKrsIu/fA+phzk7xKW2nExy8N/JHuybVbHo2U671/vDmpgWAuAiaFy9ebB06dHBBqZ8KgqmnWJdY1Cs8f/78qNdpnmlNO5Ud1apVs4cffjjX+woAhYF0VMQjTccz688Nbrl1o6r7rZC6PxSf8g+K4wGIF7n65lEKsopnAQDiKx0144/TjOmo2g7wE71Gl6ze5i550RtP8Sn/oDgegCIdNGvKKY0XzlitGgAQ/+moVA6Gn2ge6OYHV3YXLR8oik/5szieepIjeYda66/p15osGADxl5595ZVX2ty5c+28885zY4abNm1qFStWzHJaJwBA4SAdFfEsEEiwpvXzrsgmxaf8Wxxv0uw19vpX89y6todWsw5Na1AcD0D8Bs0qlKXpldavX2///ve/s9xWBcLmzJmT2/0DAORROmp2gwSloxI0o6ii+JQ/KTDu3KJmOGi+/NSWViKZYBlAHAfNCpQ1vZMCYlWsZpolAPAv0lERzzRbx96UoFsukRw44Nk6KD4FACiQoHns2LFumilNvdS4cePc3AUAoICQjop4lhYM2ZeTl7jl3l0bWFJiQp4Un1Lhu6zeEhSfAgAcUCGw7du3W8eOHQmYASBO0lFz0tNMOiqKOopPAQDyvae5WbNmtmrVqtzcFABQwEhHRTxLSgzY6Uc1zPP7pfgUACBfe5qvuuoqVz379ddfz83NAQAFiLlQgayLT3lUfEpF8HgPAAAOuKd548aN1q1bN3vsscfcuObDDjvMKlSoYElJ0e/urrvuys3DAADyOB1V8zBH9jh7Y53Vw6yAmXRUAACAPAiab7vtNle9UhUtly1b5i6xaDuCZgAofKSjIh6lBYM25+9NbrlFg8qWGMhVkhwAAAUbND/yyCO5f0QAQKFhLlTEG2VC/Llii1tufjBTXAIA4iRo7tu3b97vCQAAQAaBhARrUq9SeBkAgLgImjP6888/be3atVaxYkVr0aKFG/NcpUqVvLhrAABQjAUCCdaiAb8pAACFJ9cDg4LBoL344ot25JFHWu/evW3AgAHhatrXXXedXXjhhbZixYq83FcAAAAAAPwfNKemptrll19uTz31lG3dutVVz1ZRMI/WTZ061fr3728bNmzIy/0FAADFiH5fpKYF3SXytwYAAL4Omt9++2376aef7Oijj7bvv//ePvjgg3TX6//9+vWzNWvW2Msvv5xX+woAAIqZtGDIvpj4t7toGQCAuAiaP/74Y6tatarraa5WLfOcniVLlrQHHnjAatWqZT/++GNe7CdQrKWkptnkOWvC/3/xsz9s4uxVbj0AAAAAnxUCW7JkietlLlWqVMxtEhMTrVWrVq5HGkDuzVy4zl4dPdd27k4Nr/t10XqbuXC9vfvtQhvUq4W1bZz55BUAFAWJgQTr3bVBeBkAgLjoaVawvH79+v1up4ra6nUGkPuAecTI39MFzOIN69P6Z0bOctsBQFGUkJBgSYkBd9EyAABxETS3adPGZs2aZfPmzYu5ze+//26zZ8922wLIOaVeq4d5fyP4dL22I1UbAAAA8EnQfNlll1laWpoNGjTIPvnkE1u5cmX4us2bN9tnn31mV155pZuW6pJLLsnL/QWKjanz1mbqYY5F202bR28zgKInGAzZnL83uouWAQCIizHNHTt2tHvvvdcefPBBu/322906pUx98cUX9vnnn4e3u+GGG6xr1655t7dAMTJzwXpTJmJ2ZljRdjMWrLMurQ4qiF0DgAITDIVswbLNbrlJvUoWMFK0AQBxEDTLueeea+3bt7c333zTpkyZ4qaXUu9z9erV3foLLriA1GzgAGzfnZKtgFm0nbYHgKJGJwUb1akYXgYAIG6CZmncuLH961//yru9ARBWrlRyjnqatT0AFDWJgYAd1rBqYe8GAKAYy9WYZgD5r12TajnqaT68SfX83iUAAACg2CFoBnyqY7MaVqZU9pJBtF2HZgTNAAAAQF4jaAZ8Kjkp0Qb1arHfkje6XttpewAoalLTgvbJT3+5i5YBAChoBM2Aj7VtXM2G9DssU4+zVwxH66/p19ptBwAAAMBnhcAA5L92javbk0Oq2KTZa+z1r+a5dW0PrWYdmtZwKdn0MAMoyhIDCXZy54PDywAAFDR6moE4oMC4c4ua4f9ffmpLNyczATOAoi4hIcFKlkh0Fy0DAFDQCJoBAAAAAMjr9Oxly5bZBx98YIsWLbJdu3ZZMBi9OIfOCr/xxhu5fRgAAFCMBYMhW7h8s1tuXLeSBUjRBgDEQ9A8b948O//8823nzp0W2s9EsqRSAQCA3AqGQjZ3ySa33KhORQvsd04BAAB8EDQ//fTTtmPHDjvqqKPsnHPOserVq1tiImMrAQBA3tK594MPKh9eBgAgLoLmadOmWf369e3FF1+0QIBh0QAAIH8kBgJuFgEAAApLriLetLQ0a9asGQEzAAAAAKBIy1XU26JFC1uwYEHe7w0AAAAAAPEeNF999dW2ZMkSe+mll/J+jwAAAP4nNS1on0/82120DABAXIxpXrFihR1zzDH25JNP2scff2ytW7e28uXLx6yUfddddx3ofgIAgGIqjWAZABBvQbOCYAXImm7q77//dpdYtF1+B82rVq2yYcOG2aRJk1xV7+bNm9uQIUOsa9eu2br9nj177IUXXrAxY8a4EwJlypSx9u3b2zXXXONS0SN99tlndvPNN0e9n8MPP9zee++9PGkTAABQIbAE69GxXngZAIC4CJofeeQR84v169db//793d8+ffpYuXLlbPTo0TZgwAB79tln7fjjj8/y9qmpqW5bVQRv1aqVXXDBBbZ27Vr7+uuvbcKECfb666+7YNgzf/5893fgwIFWunTpdPdVq1atfGolAADFk06+lymVXNi7AQAoxnIVNPft29f8QnNGr1y50l5++WXr3r17OKDt16+fPfDAA24u6RIlSsS8/ahRo1zArO0feuihcIr51KlT7eKLL3b38cknn4S3VwE0Bea33HJLAbQOAAAAABB3QXOkZcuW2fTp023dunUuOK1atap16NDBDjroIMtvSsVWQNumTZtwwCw1a9a0Cy+80KVsq7f42GOPjXkf6lGWa6+9Nt2Y7I4dO1qnTp1cyrd6satVqxbuaW7cuHG+tgsAAOwTDIZs8aqtbvmQWhUsQIo2ACBeguZNmzbZPffcY99++637v8Y3RwadClT/9a9/uSA6v8yaNcv27t1rnTt3znSdt27KlClZBs2nnXaaK2QWLcgvWbKk+7tz5073d/PmzbZmzRpXBA0AAOS/YChkv/+1wS0ffFB5C1jug+axU5ba11OXpVun3y+e216cFLWoqcZU9+xUP9ePCwAohkGzenjVk7to0SLXA3vcccdZnTp1LBgM2vLly238+PH2/fffu+UPPvjASpUqlfd7bmZLly51f+vXz/xFpv2RrIqUSe/evaOuV4CstO2yZcuGxyp7c1OrnSoSph72Xbt2uTHP1113nQu+AQBA3lEMW7d6ufDygdi1J9U2bdsT8/rN2/fGvB0AoPjKVdD86quvuoBZAafGAXs9sh71/t555532xRdf2JtvvmmDBw+2/KDAVjTdVUbeuu3bt+fqvh9//HF3W50cSE5OTlcE7MMPP7Ru3brZ6aef7tLTv/vuO9ej/eKLL2a7YncsB/qDID9E7pOW/biP2eHtd7zvv7cc7+2I9/33luOxHUWhDUXhtSS0Yf+SEgPWsXmNPLmvMqWSrHL5krm6nd+PUVF6LXnL8dqWonQs4rUNvJb8IaGIHIdcB81ffvml1ahRwx5++OGoRba0TsH05MmT7fPPP89x0KzxyUqDzoqmlPJSqGLtgzedVE699NJLLjBWb7XGOnvS0tLcuquuusrOPPPM8HoFzJdccondfvvt9s0332RZeCwrak5ycqL5Tdo/mWuWlJzoy33M7vObmJjo/kZk48UNjoM/FIXjUBTaUBReS0IbClbvboe4S6w26Hve720oCschFj6b/CPe28BryR/SfH4cchLE5ypo1lzGGiecVXCo69q1a2c//vhjju+/R48e4V7kWJo2bRpOz05JScl0vXq7JeO0UPvz3HPPuYrclSpVcnM3V6hQIXydAmNdMlLBME13paJkStnu0qWL5YbeDCkpaeY3qRH7pOWAxSfvAyc1NT5/FHEc/KEoHIei0Iai8FoS2uAPtMEf+Gzyj3hvA68lf0j1+XHIyXOaq6BZgagKge2PtslNr+tdd92Vre3UGyzbtm3LdJ23LlrqdjQap6zCZe+++64LmF977TVr0qRJtve5RYsWLmjWOO4D4cc3ROQ+admP+5gT8doGjoM/FIXjUBTaEIk2FO02pKYF7Zv/Fe86sWM9l66dXzgOhYvPJv+J1zbwWvKHUBE6Drn65tEUTzNmzHDVq2PRdep1zc/iWA0aNHB/owWq3rpDDsmchpVRamqq3XjjjS5gVhVt/W3ZsmWm7ebNm+dSzqPx0sBzm5oNAACi25OS5i4AAMRN0DxgwAA37ueyyy6zd955x83R7Fm7dq1bp+vUe6tt84sCW1Xmnjp1aqbrNM5Y2rZtu9/7ufvuu23MmDEuCH/vvfesUaNGUbcbOnSoXXTRRa6NGekEgbRq1SoXLQEAANEkBhLsuMPruouWAQCIi6D5iCOOsFtvvdW2bt1qDz74oCvcddhhh7nL0Ucf7dYpPfrmm2/O9fje7ChTpoydeOKJbmqocePGhderiNhbb73lporSvmXl008/tY8//tj1MOs2tWvXjrltz5493d8nn3wy3byOX331lXt8PS+xAm4AAJBzKvpZoWwJd4k2hzIAAPktV2OaRQWxOnTo4AJN9bKqt1mBpKpLt2/f3s4///wCmbdYvb8TJkxw1bQ1BZYKd40ePdqNp1ZRL2+6KFFqtXqgVbirc+fObt3w4cPDhcXef//9qI/Rv39/q1q1qg0cONBNL6UgW1Nuqf2LFy92AbOqiatiOAAAAICCMXbKUvv6f3UPPJGdW7e9OCnqCbceHetZz071C2QfUYyDZi8V+bHHHrPCpJ5hBbuaV1kBrdLGmzdv7v6fsZdbAfOIESNcgK2gefXq1eGxz+PHj3eXWD3MCprLlSvnxjs/++yzNnbsWHfCQEXDNP2UpqZS4AwAAPJOMBiypWv3FfesX6O8BUjRBhBh155U27Qt9hSzm7fvjXk7oECCZr+oX79+uMc4K9dcc427eJSSPX/+/Bw9lgJnpabrAgAA8lcwFLJfF653y3Wrl7OAETQD+EfpkklWuXzJXN0OyC5eLQAAwLeUVVmratnwMgBEUop1tDRrfV4kJydaSkp8znEMfyFoBgAAvpUYCFjnFjULezcAAMVYrqpnAwAAAABQHBA0AwAAAABQWEGz5msGAADIjdS0oJtORhctAwAQF2Oajz/+eOvRo8d+K0jfcsstNnHiRPv5559zu38AgAPA/JUoCnbuTinsXQAAFGO5CppXrFhhGzZsyHKb1NRU+/PPP23r1q253TcAwAFi/krEO83LfHTbOuFlAAB8GTRfcMEFNmfOnHTrxowZY99++23M2+zdu9fS0tKsWbNmB76XAIBcYf5KxLtAQkKuXsMAAOSVbP0quvnmm+2cc84J/1+pfOpJ1iWWkiVLWsOGDe3+++/Pmz0FAOQY81cCAAAUQNDcpk0bmzdvXvj/6j0+9dRT7d///vcBPjwAAEBswVDIVqzb4ZbrVC/rep4BAChIucq/e+SRR6xevXp5vzcAAAARgsGQTZ+/1i3XqtrAAokEzQCAOJhyqm/fvq73+c0337Rhw4alu278+PF29tlnu+sAAAAOVPVKpd0FAIC46WneuXOnDRo0yGbOnGn169e3oUOHhq9bu3atzZo1y37//XcXQD///PNWokSJvNxnAABQTCQlBqzbYbUKezcAAMVYrnqaX3vtNZsxY4Z169bNnnnmmXTXnXXWWa6q9tFHH+3maH755Zfzal8BAAAAAPB/0PzVV19Z7dq1XS9ykyZNMl1ft25dGz58uNWoUcM+++yzvNhPFJKU1DSbPGdN+P8vfvaHTZy9yq0HAAAAgKIuV+nZy5cvt+7du1tycnLMbZSS3bp1axs3btyB7B8K0cyF6+zV0XNt5+5/phb7ddF6m7lwvb377UIb1KuFtW1crVD3EQBQtKWmBW38ryvd8tFta7t0bQAAClKuvnkqVqxoq1at2u9269evt3LlyuXmIeCDgHnEyN/TBczizeeq9c+MnOW2AwAgP23budddAACIm6C5c+fONnv2bBs7dmzMbVQETIXCOnbseCD7h0Kg1Gv1MP8vPo5J12s7UrUBAPklEEiwIw+r5S5aBgAgLtKzL7vsMvvmm29c1exTTz3VpWrXqrWvsuWaNWvs559/tlGjRrkU7SuvvDKv9xn5bOq8tZl6mGPRdtPmrbMurQ7K9/0CABQ/gYQEq8Z0UwCAeAuaGzdubE899ZTdcccdLjj+5JNP0l0fCoWsQoUK9uijj1rz5s3zal9RQGYuWG8JCf+kYmdF281YQNAMAAAAoGjKVdAsmlJK6dmaXmratGm2bt06S0tLs2rVqlm7du3slFNOsfLly+ft3qJAbN+dkq2AWbSdtgcAID8EQyFbs3GnW65ZpYzreQYAIC6CZlGRr9NPP91dUHSUK5Wco55mbQ8AQH4IBkPhqQ97d21ggUSCZgBAwTrgeRu2bdtmP/74o3344Yc2YcIEt27RokUuRRvxqV2TajnqaT68SfX83iUAQDFWpUIpdwEAIK56mrdv327//ve/3XjmlJR96bl9+vSxbt262V133WUbNmxw455btmyZl/uLAtCxWQ03D3N2ioGVKZVkHZoRNAMA8ofmZe7epnZh7wYAoBjLVU/zrl277KKLLrIPPvjAFfw66aST0vUsq2r2smXL7OKLL3Z/EV+SkxJtUK8Wtr8EOF2v7bQ9UBxoejUvTVRe/OwPmzh7FdOuAQAAFGG5CppffvllmzNnjp199tn2/fff25NPPpnu+jfffNOuvvpq1xv94osv5tW+ogC1bVzNhvQ7zPUkR/Lqr2j9Nf1au+2A4mDmwnV2w4gJ9vpX88Lrfl203l75Yq5b/+vC9YW6fwAAAPBR0Dx69GirXbu23Xvvva5XOZprrrnGGjRoYFOmTDnQfUQhade4uj05pJtdclKz8Lq2h1azy3q3cOsJmFGcAuYRI3/PNGTBS7DR+mdGznLbAchbqWlBG/frCnfRMgAAcRE0r1y50g477DBLTMw6LbdJkya2evXq3O4bfECp151b1Az///JTW7o5mUnJRnGh1OtXR8+1/dXG0/XajlRtIO9t3rbHXQAAiJugWfMvK3Den+XLlzNXM4C4NnXe2mwVxRNtN20evc1AXgoEEuyIlge5i5YBAIiLoLljx442e/ZsmzRpUsxtfvrpJzfuuUOHDgeyfwBQqGYuWB8ey78/2m7GAoJmIC8FEhLsoCpl3EXLAADERdB81VVXWXJysl155ZU2YsQImzp1qlu/e/du++OPP+z555+3a6+91pKSkmzQoEF5vc8AUGC2707J0bzl2h4AAADFfJ7mpk2b2tNPP20333yzPfvss+6SkJBg33zzjbto+qmSJUvaI4884sY+A0C8Klcq2fUgZydw1nbaHkDeCYZCtn7zLrdcrVJpepuLiLFTltrXU9NPSxo5feltL05yvy0z6tGxnvXsVL9A9hEADiholmOPPdbGjh1rH374oetpVsGvtLQ0q169urVv395NR6UK2wAQz9o1qWbTs5lyrd97hzepnu/7BBQnwWDIJs7eV1S0d9cGFkgkaC4Kdu1JtU1ZFHfbvH1vzNsBgC+D5pNPPtm6detmd911l/u/guSqVataw4YN7YorrnAXACiKOjarYe9+uzBbxcA0f3mHZgTNQF6rWK5kYe8C8ljpkklWuXzJXN0OAApatj55VqxYYevXrw///8ILL7RTTz3V/v3vf+fnvgFAodP0aoN6tXDzMGeVoa2+L23HdGxA3kpKDNix7eoU9m4gjynFOlqatTKyk5MTLSUlLdv1JAAUvrFFfMhFtoLm0qVL28yZM23evHlWs+a+OXv37t1rmzdvztaDVKpU6cD2EgAKUdvG1WxIv8PcPMyRPc7eWGf1MCtg1nYAAADFza4iPuQiW0HzkUceaaNHj7a+ffu6/+ssgcYz67I/2lZTTwFAPGvXuLo9OaSKTZq9xl7/ap5b1/bQatahaQ2Xkk0PMwAAKK5KF/EhF9nay7vvvtsSExNt1qxZrod51apVVqpUKatcuXL+7yEA+IQC484taoaD5stPbWklkgmWgfyUmha0X/5Y45aPaFnTpWsDAPylZxEfcpGtoHnNmjV2zz33WLly5dz/mzVrZieeeCJjmgEAQL5bv2XflFMAAPg2aB44cKC1bdvWRowY4f4/ZMgQFzgDAADkp0AgwVWx95YBACho2cpxyljwS8HzN998Y36hdPGbb77Zjb1u166d9e/f3yZOnJjt23/22WfWtGnTqJfzzjsv0/bjxo2zc845xz1W165d7Y477rANGzbkcasAAEAgIcHqVC/nLloGAMCXPc3VqlWzCRMm2JtvvmkHHXSQW7dy5Ur7+uuvs/UgPXr0sPyiqbAUJOtvnz59XAq5ipYNGDDAnn32WTv++OP3ex/z588P96irUnikWrVqpfv/F198YTfeeKPVr1/fBdR6Hj7++GM3d/XIkSOtQoUKedxCAAAAAICvg2ZVzX7++eftkUceCVfEnjZtmrtkx9y5cy2/PP300y5wffnll6179+7h4Ldfv372wAMP2FFHHWUlSpTI8j4WLFjggu1bbrkly+127Njh7rNBgwYuQPbGeH/00Ud255132gsvvLDf+wAAANkXDIVs09Z905hUrlCS3mYAgD+D5muvvdbq1q1rv/32m6WkpNioUaNcT2v79u2tMCmI/eSTT6xNmzbhgFk0l/SFF15ow4YNcz3kxx577H57mhs3brzfx1MP9pYtW+yGG24IB8xy5pln2iuvvOL25aabbrJAgMqeAADkhWAwZD/NWumWe3dtYIFEgmYAgA+DZvUsq+dWF1HQrMJgXs9zYfGmwOrcuXOm67x1U6ZMyTJo1nhtVQc/5phj9vt4SsGOvO9InTp1svfff9/+/PPPbAXgAAAge8qWTi7sXQAAFGO5mk1aY5urVq1qhW3p0qXur3q9M6pTp477+/fff+83NVuCwaBdc801Nn36dNu1a5cdfvjhdt1111nr1q3D2y5btsydQFCve0beOj0eQTMAAHlD8zKf2KFeYe8GAKAYy1XQrF7V7JgzZ46rbJ2dYly54VX1Ll++fKbrvHXbt2/PVhGwDz/80Lp162ann366C46/++4710v94osvugrZsmnTJlcoLNoYaS9de9u2bQfUJj8O1YrcJy37cR+zw9vveN9/bzne2xHv++8tx3s74nX/hTb4A23wh6LQhqLSDtrgD7TBHxKKQBuyHTQrSD7llFPsvvvuy3SdxvHWq1cv6vjm//znP67adE4LgWl8slKms6K5otXrK9GCWG/dnj37iofEkpaW5nqlr7rqKjc22aOA+ZJLLrHbb7/dTa+l+0tNTY1ZVCy7j5cVNSc5OdH8Ji30z3JScqIv9zG7z29iYqL7G4poU7zgOPgDx8E/aIM/0AZ/KAptKCrtoA3+QBv8IcHHbchJIJ+toHnr1q22c+fOqNfddtttduqpp+ZpUTBNUZVxbuiMNIeyl56t4mQZaayzZJxCKiMFxrpEO1GgKax0UkAp2126dLFSpUrZxo0bo95Pdh8vK3ohpaSkmd+kRuyTluO1zJn3Zk1NTfPdmzY7OA7+wHHwD9pQPNqQFgzalDlr3XKnFjUsMR+KbXIc/KMotIM2+ANt8IcEH7chJ/uTq/Ts/HbXXXdlazulVMdKifbWRUvdzq4WLVq4oHn58uXu/5qD+a+//nJBenJy+qIkXhr4gTye+O3FlHGftOzHfcyJeG0Dx8EfOA7+QxuKdhuCQbPVG3eGlwP5mOLHcfCPotAO2uAPtMEfQnHehnjtJHE0X7J4QW0kb90hhxyS5X3MmzfPJk+eHPU6L9XaS73W46lgmMZp5/bxAABA9mle5sObVHcX5mgGABSGuA6aW7Zs6VKmvamgImlMsmhqrKwMHTrULrroIlu7dl/qVySlZUurVq3cXy8F3bvvSAq8K1WqRNAMAEAeCgQSrH7N8u6iZQAAClpcB81lypSxE0880aZNm2bjxo0Lr1cRsbfeestq1arlioplpWfPnu7vk08+aaGInIGvvvrK3ecRRxxhjRo1cutOOOEEK1u2rL388stunLfno48+clNNnXXWWeHiZAAAAACA+OfLMc05oZ7iCRMmuGravXv3duOOR48e7aaHeu6559KNPVZvsHqJVeSrc+fObt3AgQPd9FIff/yxLVq0yDp06GCLFy92AXONGjXsoYceCt9ePck333yzqyKuqakUcK9evdoF2A0bNrTBgwcXynMAAEBRFQyFbNuOfcU2y5ctQYo2AKDAxXVPs9SuXdvef/99O+6441zwq15fjT1+9dVX7Zhjjkm3rQLmESNGpEuv1vzK7777rg0YMMA2bNjgeqhnz57tpp8aOXKk1a1bN919nHfeeTZs2DCrWLGivf322y6F+4wzzrA333zTBewAACDvBIMh+2HmCnfRMgAABS3ue5qlfv36Nnz48P1ud80117hLRgqcb731VnfJjl69erkLAADIf6VKFImfKwCAOJXtb6GJEye6glk5ue7PP/88sL0DAADFWlJiwE7qXL+wdwMAUIxlO2hev369u+T0OgpjAQAAAACKdNCs8boAAAAAABQ32QqaVW0aAACgoKUFgzZ9/jq33L5pdUsMxH0NUwBAnOGbBwAA+FYoZLZy/Q530TIAAAWNcpQAAMC3NC9z60bVwssAABQ0gmYAAOBbgUCCNaxdobB3AwBQjJGeDQAAAABADPQ0AwAA3wqFQrZjd6pbLlsqiaksAQAFjp5mAADgW2nBkH07bZm7aBkAgIJGTzMAAPC15CTO8QMACg9BMwAA8K2kxID16tKgsHcDAFCMceoWAAAAAIAYCJoBAAAAAIiB9GwAAOBbacGg/bZog1tuc2hVSwxwvh8AULD45gEAAL4VCpktXbPNXbQMAEBBo6cZAAD4ViAhwVoeUiW8DABAQSNoBgAAvhUIJFjjupUKezcAAMUY6dkAAAAAAMRATzMAAPCtUChku/emueVSJRItgRRtAEABo6cZAAD4VlowZGOnLHUXLQMAUNDoaQYAAL5GATAAQGEiaAYAAL6VlBiwU488pLB3AwBQjJGeDQAAAABADATNAAAAAADEQHo2AADwrbRg0Gb/tdEtt2pYxRIDnO8HABQsvnkAAIBvhUJmi1dtdRctAwBQ0OhpBgAAvq6c3ax+5fAyAAAFjaAZAAD4ViCQYM0O3hc0AwBQGEjPBgAAAAAgBnqaAQCAb4VCIUtNC4bnbE4gRRsAUMDoaQYAAL6VFgzZ6ElL3EXLAAAUNIJmAAAAAABiID0bAAD4VmIgwU7tdohbJjMbAFAYCJoBAIBvaQwzwTIAoDCRng0AAAAAQAz0NAMAAN8KBkM2Z8lGt9zi4Cpu3mYAAAoSPc0AAMC3gqGQLVq+xV20DABAQaOnGQAA+FYgIcEOrVsxvAwAQEErEkHzqlWrbNiwYTZp0iTbsWOHNW/e3IYMGWJdu3bd720vvPBCmzJlSpbb6L6uueYatzx9+nTr379/1O1q1qxpP/74Yy5bAQAAMlI6dqtDqhb2bgAAirG4D5rXr1/vglj97dOnj5UrV85Gjx5tAwYMsGeffdaOP/74LG/ft29f69SpU6b1aWlp9tprr1lqaqp16NAhvH7+/Pnu79lnn201atRIdxs9NgAAAACg6Ij7oPnpp5+2lStX2ssvv2zdu3d36wYOHGj9+vWzBx54wI466igrUaJEzNufccYZUdc///zztmfPHtfD3KVLl/D6BQsWuL833nijVapUKc/bAwAA/hEKhcwbyqzsbE1BBQBAQYrrQmBKxf7kk0+sTZs24YDZS5NW2vXq1attwoQJOb7fRYsWuV7qFi1a2JVXXpnuOvU0V69enYAZAIACkBYM2WcTFruLlgEAKGhxHTTPmjXL9u7da507d850nbduf+OVo/m///s/S0lJsXvuuccSExPTXaee5qZNmx7AXgMAAAAA4kVcp2cvXbrU/a1fv36m6+rUqeP+/v333zm6z6lTp9q4cePsxBNPtHbt2qW7bsWKFbZ9+3YrXbq03XbbbTZx4kTbsmVLuPDYkUceeUDtAQAA6SUGEqxXl4PDywAAFLS47mnevHmz+1u+fPlM13nrFOTmxKuvvur+Dh48ONN1XhGwb775xhYvXmy9e/e2E044webOnWuDBg2yUaNG5aodAAAgOo1hTk5KdBfGMwMACoMve5o1PnnNmjVZbqOeXe/LM1qhL2+dinll17Jly2z8+PGumnbr1q0zXb9r1y47+OCDXZVubwoq+fPPP1017fvvv9+OPvpoq1KliuWWH38PRO7TviIsFpe8/Y73/feW470d8b7/3nK8tyNe919ogz/QBn8oCm0oKu2gDf5AG/whoQi0wbdBc48ePcK9yLFoXLGXnq3xxxlprLMolTq7Pv30UwsGg3buuedGvb5Xr17uklGjRo3s4osvdsXDvvvuOzvrrLMsN/RiSk5OP4baD9Ii6q4kJSf6ch+z+/xqjLr+epVY4wnHwR84Dv5BG4pHG4LBkM1busktN6tf2c3bnNc4Dv5RFNpBG/yBNvhDgo/bkJNA3pdB81133ZWt7T788EP3d9u2bZmu89ZFS92O5fvvv7dSpUrZscceazmlStuyfPlyyy29kFJS0sxvUiP2ScvxmtPvvVlTU9N896bNDo6DP3Ac/IM2FI82pKYF7Y+/NrjlQw4qb0mJef+u4zj4R1FoB23wB9rgDwk+bkNO9seXQXN2NWjQIGag6q075JBDsnVf69atszlz5rgxymXKlIm6jdKwNY3VEUcckamqtpcGXrJkSTsQfnsxZdwnLftxH3MiXtvAcfAHjoP/0Iai34YGB1VI9zj5hePgH0WhHbTBH2iDP4TivA3x2knitGzZ0vUMq+J1Rt5UU23bts3Wff32228WCoWsQ4cOMbd59NFHbcCAAW6qq4ymT5/u/rZq1SoHLQAAAFlJDASszaHV3EXLAAAUtLj+9lGPsKaGmjZtmpsmyqMiYm+99ZbVqlXLFRXLDlXA9gLxWHr27On+Dh8+PN046hkzZtgHH3zger67det2AC0CAAAAAPhJXKdny9ChQ23ChAmumramgKpQoYKNHj3aNm3aZM8995wlJyeHt508ebLrgVZ17M6dO6e7H6+oWM2aNWM+Vt++fd19a37m0047zY466igXoH/77beuWvcTTzyRKW0bAAAAABC/4rqnWWrXrm3vv/++HXfcca5y9UcffeR6fDXf8jHHHJNuWwXMI0aMCKduZ3fOZ48C4hdffNGuu+46S0tLs3feecd++eUXNw565MiRpGYDAJAPhcA++3mxu2gZAICCFvc9zVK/fn2XMr0/mls5cn7lSC+//HK2Hks9yldddZW7AACA/BeM5+oxAIC4VySCZgAAUDQlBhKsZ6f64WUAAAoaQTMAAPCthIQEK12SnysAgMIT92OaAQAAAADIL5y6BQAAvhUMhuzPlVvccqPaFS1AijYAoIARNAMAAF8XAftj8Ua3fEitChYwgmYAQMEiaAYAAL6VkGBWv2b58DIAAAWNoBkAAPhWYiBghzepXti7AQAoxigEBgAAAABADATNAAAAAADEQHo2AADwrdS0oI2dstQt9+xU35ISOd8PAChYBM0AAMDXUlKDhb0LAIBijKAZAAD4VmIgwU7oUC+8DABAQSNoBgAAvpWQkGDlSicX9m4AAIoxBgYBAAAAABADPc0AAMC3gsGQ/b16m1tucFB5C5CiDQAoYATNAADAt4KhkM36c71brl+znAWMoBkAULAImgEAgG8lJJjVrlY2vAwAQEEjaAYAAL6VGAhYp+Y1C3s3AADFGIXAAAAAAACIgaAZAAAAAIAYSM8GfGjslKX29dRl6daFQqHw8m0vTnJzl2bUo2M969mpfoHsIwAUhNS0oH07bblbPqFDXUtK5Hw/AKBgETQDPrRrT6pt2rYn5vWbt++NeTsAKGp27+WzDQBQeAiaAR8qXTLJKpcvmavbAUBRonmZj21XJ7wMAEBB4xc24ENKsY6WZq2M7OTkREtJSbOIbG0AKLICCQlWsVzOTyICAJBXGBgEAAAAAEAM9DQDAADfCgZDtnzddrdct3o5UrQBAAWOoBkAAPhWMBSyGQvWueXa1cpawAiaAQAFi6AZAAD4lmo51KxSJrwMAEBBI2gGAAC+lRgIWJeWBxX2bgAAijEKgQEAAAAAEANBMwAAAAAAMZCeDQAAfCs1LWg/zFzhlo9tV8eSEjnfj/gRCoUsGAxaMJhm8UT1A0KhgKWmBi0UsrhEG4pfGwKBRAsEApaQDwUwCJqRztgpS+3rqcsyfeB7bntxUtQXYo+O9axnp/oFso8AgOJlx66Uwt4FIEf022nnzm22bduWuAuYgXgWCCRauXKVrHTpsnkaPBM0I51de1Jt07Y9Ma/fvH1vzNsBAJDXNC/zUa1rh5eBeLBp03rbvn2blSpV1kqVKuN+yOdH71f+9xBaXKMNxacNIZfVkWa7d++0rVs3WErKHqtYsWqe3T9BM9IpXTLJKpcvmavbAQCQ1wIJCVa1YqnC3g0g2/TDfceO7a63q1y5ihavCNb8gTbkjE5Sbd+ebNu3b7Hy5Su5E1Z5gUgH6SjFOlqatV7sycmJlpKSFvdvXAAAgPySlqbfSiErWZKTPUBhKFFC773N7r1I0AwAAIq8YChkq9bvcMu1qpV1Pc9AfOC1ChSG/BgKQQlKAADgW8FgyKbOW+suWgYAoKDR0wwAAHytWsXShb0LAIBijKAZAAD4luZlPrJ1rcLeDQBAMVbk0rPffvtta9q0qe3YsW/8U3aNGzfOzjnnHGvXrp117drV7rjjDtuwYUPUbWfOnGmXXHKJdezY0Tp16mTXXnutLVuWfm5jAAAAoLh59dUX7cgjO2TrMmTI4ELbz/Xr19mYMZ9bvNi2bZt9/PGH+91ObdJzO3Lk+1aYjjyygw0ceKEVFUWqp3nq1Kn2f//3fzm+3RdffGE33nij1a9f38477zxbuXKlffzxx+7+Ro4caRUqVAhvO2XKFBswYIBVqlTJzjjjDNu6dau7/eTJk922devWzeNWAQAAAPGhXbv2mdZ9+eUXtnr1KjvrrPOsXLly4fW1au2bg72gbdq00fr3P9M6depsp5zSx+JB//79rEaNmnbGGWdZPLj00susWrXqVlQUmaB59OjRduedd9ru3btzdDv1SD/wwAPWoEEDF/R6b+SPPvrI3d8LL7xgt9xyi1sXDAbtnnvusbJly7pta9as6dafeuqpdumll7qA/emnn86H1gEAUDylpgXtp1mr3PJRrWu5dG0A/nX44R3cJdLMmdNd0Hz22ecVWqAcSfHCzp05y0otbAr0FTTHi4EDL7eiJO6/eTZu3GhXX321DR061KpUqWIHH3xwjoPtLVu2uHTryDNfZ555ph1yyCH2ySefuGBZJk2aZIsXL7azzjorHDBLly5drFu3bvbtt9+6+wIAAHlny/Y97gIAQGGI+6B54cKF9t1337lUaQW4kcFsdigFWzp37pzpOo1X1rjmP//8c7/bal1qaqob7wwAAPJGIJBgXVsd5C5aBlC0eGNwv/vuG7vuuivt2GO72Jln9rG1a9eEx/I+++zTdtZZp7nrzjijlz311P/Zli2bM93XypUr7LHHHrKzzjrVbXvCCd3tsssutq++Gp3u8XS9jBv3vXtsb2yzlh999F82Y8Y0u/LKAXb88d3s9NNPttdee8lCoZAtWDDP7eMJJxzp9mPEiKdsz570J/TU2abxxJdc0t+OO66bnXLK8XbHHTfbX38titruadOm2DvvvGHnnHO622f9feut/4Q77bQv2k7mz5/rljVufH/S0oJuO+2/2nHFFQNs4sSfo277zTdf2eDBl7h29ex5tA0dOsRmzfo16raffDLSLrroHHefOibad6Xfa7+0r7HGND/00H1unYa2/t//PWynntrTPT+DBl1kP/44LtPjfPDBuzZgwAV24ond3T5dc83l9vPP462wxH3QrHHIn376qT3yyCPpxh5nlwp4aQLsaGORvXV///13eFvvMTOqU6dOum0BAMCBCyQkWI3KZdxFy0BRGXagiwIxj+Yh17q0/wVLBbVtMHLbiOWCpkB4+/btduaZ51rLlq1cKrICZgWv7733ltWrV8+NiW7atLmNHPmBXX75gHSB84oVy10A9s03X1rr1m3snHPOtyOPPMr+/HOhPfjgvfbtt2Pddo0bN3H3Iw0aHOLG3mqd548/frcbb7zGqlSpaqef3s/FCQqan3jiUbv66sssOTnZjSsuWbKk/fe/b9vbb7+erh0PPHC3Pfnk/7ljcPrpZ1j37sfY1KmT7fLLL7XZs2dlavdzzw23119/1dq2Pdzd7+7du+zFF591wagonV37KFWrVnPL0caNZ/TOO6+7/eva9Ujr0eNk+/vvv+zWW29wAXIkPdb999/lnstevU61E0882Z0cUJA6fvz36bYdPvwJe/zxR2zv3r126qlnWKtWh9lLLz1n//nPy5Zd119/lU2Z8osdd9wJduKJPd3JhDvvvNl+//238DZvvfW6DR8+zC3rGJx0Ui/7++/FdtttN9r48T9YYYj7Mc21atVyl9zatGmTlS5d2kqUKJHpOi9dW29Y2bx53xuzfPny+90WAAAAiOaLifs6WU7ufLCVLJHolhcu32xzl2yygw8qb+0a/1NA6cvJS12vYY+O9axMqWS3bvGqrfb7XxusbvVy1qFZjfC230xdZntS0uy4w+tahbL7ftsuXbvNfl243mpVLWudW/yTkfn9jBW2c3eKHd22jlUuX9KtW7Fuh9Wr8c9wxYKkYPS55162kiVLhde9+OKI/wVLd1vv3qeF13/99Vf2wAN3uYDz9tvvcevefvsN27p1iw0f/kJ4TLXOs02e/IvrOVWweMIJPa1x46Z29tnl7cMP33NBc8axt4sX/2U33HCz9et3jvt/jx6n2MCBF7ge1kGDrrBLLhnk1p9//sXWt28v++abseH7+O67r11wruJit956lyUm7ju2/ftfaIMGXWwPPnifvfvuRxYI/NNvuWrVSnv99XetTp19nXU6aXDeeWfYqFEf2YUXXuqCZt2/AlMV1sruWGENGX355Tdce+W88y6wwYMvtaefftyOPvo4F/vMnv2769Xu1OkIe+SRJ9yJALn00kGuh/6RR/5lHTt2tjJlytq8eXPsww//a4cd1saGDRvh4ic58cST7JZbrs/2cdbjvvXWB1aq1L7j3L59J3cs9fzqvuW//33LPR8vvfS6JSXtC1fPPru/K4b28ccf2NFHH2sFzZdBc/fu3W3Nmn0pGbEMGTLErrnmmgN+LKVURwuYxVvvpV2kpKSkW5/Vtrnl15Po3n75df+ygzb4Q7y3IXK/tRzv7YjX/RfaUDzaoJ6vtRt3ueUaVUrnS28zx8E/iko7kHNdunRLFzDrN/rYsWNcL3BkwCw9epxk7777pgtQb775DhdYqTfysMNaZypC5vXKqqMsOxQ4nnZav/D/mzZtZiVKlLSUlL127rkXhNdXrFjJZZouX/7PtLOjR3/meqaHDLkhHDBL/foNXC+uAvXff59lbdq0DV937LHHhwNmUZDcoEFDW7RogXsOkpNzF6717HlKOGD29uG0085wPdjq+e7W7SgbM+Yzd92VV14bDpi9Hu1zzz3f9fb+9NN4d19KcVfv+eWXXx0OmEU92QqsdZ/Z0a/f2eGA2TvusnLl8nTZEZs3b3Lp9vXr76tXpefovfc+zlFF7rz8nebLoLlHjx7hXt1YNBdzXtBBUzGxaJR6IN4LwzvAXvCc1ba5oYOanPzPG8xPtG968+tvIWbuHBDa4A/x3oa0iH1OSk707Xu2qB8HoQ3Fow1KH50yb61bPu2oQ/KlejbHwT+KRjsCLnDK6gd7n24N3N/EwL7tpEm9SnZo3YqZbnfKEfUzbduwdgVrUKt8pm17dKqXaduDa5Z3vccZtz2+/b6hhaoV4K2vW6NsxIkLLYTy/GRIrOdEAWjkdcuWLbFdu3ZZSkqqvfZa5jG8oVDQdVYtW/a3NWx4qLVt29ZdlGa8cOECF8wuXboknParMcLRTspk3J+DDqqVKVAtXbqUlS9fzv2NtC+YTgnfh9KaFXx+9NF7mfbXCwoXLZrv9tOjoDDjPngZrKmpKVaiRHK645DdILBVq9aZtm3RoqX7++efC1zq+oIF893/f/jhG/vpp/RpzwpY9+3vAjvppFNs7tw54fvIeL9Kh1fQHO347luXELO9el5Fx9lbr+Beae8XXHCWtWjRyo44oqt163akNWnSLFtt9+4nKSmQ5e+0nATUvgya77rrrgJ7LI2D/uuvv9wLXmkhkTSuIjId2xszrfWVK1fOctvc0JdDSkqa+ZH35ZWamha3X2K0wR/ivQ2pEe9RLcdrYYh4Pw5CG4pHGxQ0Vyiz7/tZ35GhYN4/CMfBP4pCO1JS9o0p1v7HakNiRHqut40Ci8T//YqPvF2BbmsJEdvsa0Ne8O4nq+dEvcyR13lDHjUW97XX/op531u3bnO3U2r2008/4Xqf09LSXLvVO9mhQyebM+eP8DGJ3J+My1KqVOmo+5icXCLmvnvrtc967Ndeiz3GV9tE3o/ij1j3qx7XyP2Otr+xVK5cJdO2ZcqUcX937tzlrtu+fd9z/Oab/4l5PyrcFQop3XuzC+Z1oiDj/VatWj3m8d33/1AW7fVem/+0U73ZOnaffvqxG2OuseCvvPKC64G/9dY7w2ncsXj3k5qqEyWxY6ucvL59GTQXJM3PPGPGDFu1alWmAl/Ll+87I6Spp7xtvfUqRpDVtrnl9y+IrD7s4gVt8Id4bUPGL654bEMk2uAPtCE2/bDXmMvIx8kvHAf/KCrtQO6VLr0vwNP44DvuuHe/2//rX/fYpEkTXPqvilk1bNjIypYtY3v27HXjZQtqn8uXr2AffvipFbYdO/Z16EVav359uo5AZcgqtf277yakSyePpkyZsm78tU4KZNx2x468nfNaJzz69DndXTZsWO8qjI8b951LFb/11qE2cuQX2cruzcvPkXjtJMkz7dvvG+cwZcqUTNdNnjzZKlWqFA6Es9pW6/QCOuyww/J9nwEAAICiTGm8qhk0f/68qNdrbO6bb75mO3fudL23CphbtjzMbrjhFlfV2etVXbJksfsbWVE8Ml04Lx16aGNbs2ZV1PHTqkSt3tIlSwpmph2limfkVe/20pwbNWrsxk0vWrQw07YzZ053lbXnzJnt/q+q5QqYNe1VRt42eUE925oqS9NYeeOrNaZahcqOO+5El1GwePG+6YALUrEPmk844QQrW7asvfzyy+4geT766CM3fdRZZ50VfmNp3ubatWvbe++9ZytXrgxvO2nSJJswYYL17NnTBdkAAAAAck9jgxUkacqoDz5IP0b4l18m2gsvjHDVqhUcK+VXFakVUCkIjOwBVcq2pKX9s96ryLx3b+Y6RQfi5JN7u7HTTz7573T7sXr1anv88UfdOF1vvHJOaZ9VjCy7vvxytOsZjgyiFYiq0JhXHE29+N5UUjr54NFJCE0tpcragcC+XuVevfZtq0B69+7d4W01N/OPP+bdNFA6nh999L69/PLz6WIzPa+au1vHWdORFbRilZ6tnmP1CCv47dy5s1unIPfmm2+2++67z04//XQX+OqF/dVXX1nDhg1t8ODB4durJ/nee++1q666ys444wzr06ePezN+/vnnVrVqVbvpppsKsXUAABQ9GtM8cfZqt9y11UH5UggMgD9dffX1rpCXgrqffhpnzZq1sDVrVrsgTUH1zTffGS7W2737sS6Fd/Dgi904Zv1G//nn8S4AVAeZpmCKrHytXuzp06fYM8886aYwat36n+JcuaUgVI/5/fffuGC/Y8cjXKD7/fff2rZtW+36629yPae5Ub16DTcdloJvVZxW9eusKAV70KAL3ZRQCoh/+OFbt15TdHnp1ao0rimuPvrov3bhhWe7+1VwrrmQ161b6+a6btasebiwmOZMVqr7JZf0d8W5Nm3aYOPGfe9qOqmI8/5SvLNDjz9o0OVuruuLLjrHzXOtcdQzZkx1hctOP/3MHFXQzivFKmhWwDxixAg3XZUXNMt5553nXlivvPKKvf32267Il4Li66+/Ppzz7znmmGPcds8884x98MEH7k147LHH2o033uiq/gEAgLy1ces/vRoAig/9JtdcvUrD/vHH8S69WAWuFCBfcslAVzXbc/vtd7tgSsH1Rx99YNWqVbM2bdrZRRcNtP/+9203ZdJffy1yt1HP9NCht9grr7xoo0Z96Hp/8yJoVnbqgw/+20aO/MDGjPncPvtslKu43ajRoda//0VueqbcUtr5U0/9n40e/amrqr2/oPnKK69x01t99dUXrgp5q1Zt7IorrrbmzfdV0PYokNe0WgqG9RwpaNX0VCrGpbTojPtQu3Zd+/zzUfbppyOtWrUadvXV17mx0poCLHLKsAOhObIrVarsgnllE6hnW+n6mj9bQXNhSAhFJvijUGni+o0b83YgfV7xpsNylUvj9BVDG/wh3tuwZ2+aXTlsvFt+4cajrUQcTzkVz8dBaEPxaIPmaV6zcV/aYM0qZfJtnmaOgz8UhXakpu619etXWdWqmrqohMWr+J72ax/akHc2bFjvTjZUqFAx03UPPnivC7g///xrd1KjsNug3v0NG/b/HqxSpawlZjN7iRwnAADgWwqSa1Ut6y75ETADAPZv7Ngv7ZRTjg8X6PKsWLHcfvxxnJsOKlrAXFQUq/RsAAAAAEDOnHBCD5cm/+9/P2QTJ/7s5lHeuHGDqwqudHGluxdlBM0AAMC3lJ69ccu+Mc1VKpaitxkACkGNGjXtlVfedBW1VTF7woQfrVy58q7Y2YUXXurGRRdlBM0AAMC3gsGQ/fz7Krfcu2sDCyQSNANAYahbt56rvl0cETQDAABfK18mfospAQDiH0EzAADwLc3LfHz7uoW9GwCAYozq2QAAAAAAxEDQDAAAAABADKRnAwAA30pNC9rkOWvccucWNV26NgAABYmgGQAA+Nq6zbsKexeAQjd2ylL7euqyHN+uR8d61rNT/XzZJ6C4IGgGAAC+FQgkWPumNcLLQHG1a0+qbdq2J1e3A3BgCJoBAIBvBRISrF6NcoW9G0ChK10yySqXL5luXSgUss3b97rlSuVKWEJCQtTbATgwvIsAAAAAn1OKdcY06z170+zKYePd8iODu1jJEomFtHdA0UY1DQAA4FvBUMilpOqiZQD+9+qrL9qRR3bIdDn66M7Wu/cJdsMNV9vPP/+Y6/sfM+Zzd38jR76f6/v4+usvbfXqVVZYMj5+XrTpQG3bts0+/vjDQnt8P6OnGQAA+FYwGLLxv65wy727NrBAIuOaAUlJTQtXlpcXP/vDOjSrbh2b1bDkJH/0OB911NF26KFNwv9PTU219evX2ffff2NTp062m2663U4/vV+O77dx4yZ26aWXWfPmLXO1X889N9zeffdNe/PN/1phiPb4B9qmvNC/fz+rUaOmnXHGWYW2D35F0AwAAHytTKnkwt4FwFdmLlxnr46eazt3/1Pk67c/19uvi9bbu98utEG9WljbxtWssB111DF2yil9Mq0/9dS+dtVVg+z554fbSSf1slKlSuXofhs3buouubVp00YrTNEe/0DblFf7paAZmZGeDQAAfEvzMmvKHF2YoxnYFzCPGPl7uoBZvNELWv/MyFluO7867LA21qpVa9uxY4fNmvVrYe8OsF/0NAMAAABxkpKtHub9je7X9druySFVfJOqnVGNGvumktuyZXN43Z49e1za8jfffGWrVq20MmXKWLt2HWzAgMusYcND043/ffjh++2GG262fv3Oces0Hvjkk3tb796n28svP2fz5s2x5OQS1qlTZ7vyymvtoINque3OPLNPeCzxRRed69Z/9NHnWe7r0qVL7LXXXrJp06bYzp07rHbtutar16l29tnnWWLiP8+vrnvppedt8uSJtmbNaitbtpy1adPOpV03anRolo8fq029e59mPXqc7Nq0YMF8K1++guup130uXDjfnn32afvjj9+tQoWKdtxxJ9pll11pJUuWTNd7/Pbbr9vEiT/bmjVr3NR9derUs1NO6W1nn93fVVyfMWOaXXvtFW77+fPnusfV/Q8ceLlbt379Otd+3YeOV/XqNeyEE3raRRcNyHGWQLzilC0AAAAQB6bOW5uphzkWbTdtnn97m5cvX+7+KgCTPXt22/XXX+mKiJUoUdJOP/1MFzD//PN4Gzz4Eps5c/p+71MBn+4jMTHJjctVoPrdd9/YjTde46bnEgW63jhrPYb+nxUF35dddpH9+OMP1qFDJzvzzHOtdOlS9uyzT9k999wWvl+5++7b7aOP/mv16h3sAtJOnY6wiRN/siuvHGirV6/O1eMrINb+V6lS1Y3/VpCrAPaJJx61q6++zJKTk11bFSj/979vuwDZs3XrVvfcjRz5gTVq1Ng9lgJrnZB45pkn7e2333Db1apV2wXJUrVqNbfcrl17939tO2jQRfbFF59aixatXLtq1apjb775ml1//VW2d+++Kc+KOnqaAQCAb6UFg+Ef/ipylBjgfD+Kr5kL1pumYs5OIXltN2PBOuvS6iDzG1XOVoBbtWpVa9nyMLfunXfetN9/n2WnnXaGDR16a7gH97ffZrpe0H/96x57//1PXJAYy19//WnXXHODnXPO+f9bE7KhQ6+xKVN+cb2p7dt3dEHfwoULbNGiBXbGGWem68HOSAHxgw/eZ2lpafbSS2/YoYc2Dl/3738/ZJ99NsrGjh3jxmX/9dci18Os5bvuuj+8nQLthx66z7766gu75JJBOXp8Wbz4r3S9zz16nGIDB15gn3wy0gYNusLdp5x//sXWt28v++abseEe4k8++cgFvXfccW+6seXa9oILzrJvv/3KLrzwEhc06zb/+c/LVq1a9fDt5fHHH7WNGzfYE08Mt44djwivV9D80kvPucwAbx+KMoJmAADgWwoOVm3Y8b/l6oW9O0Ch2r47JVsBs2g7bV+YfvppnAvaPCkpKS6wVXCpoHjo0NvCQfCXX462MmXK2jXXDE2X8qz0ZgV8n3/+iQt+u3U7KubjlS5dOhxcinpljziiq7vdihXLXdCcE+rl/fvvv/7XO/xPwCxXXDHE7ZP2W4GyKv17qdw7dmx3qdmiNGb12ua2wJZ6kE877Z8K402bNnM98Skpe+3ccy8Ir69YsZLVqVPHli9fFl6ntmu90rsjHXxwA9dzvWnTpiwfW2nZOlZHHXVMuoBZ+ve/yN5//x378st9JwOKOoJmAADgW4GEhHAVYC0DxVm5Usk56mnW9oXpp5/Gu4tHAXLlylWse/djXSCqgmDeWOBVq1a44DLaGNnWrdu6AFW9s1kFzeoxTUpKH954wasC9pyaP3+e+7ts2VKXNp5R6dJlbNGi+W5ZqeAqbjZ79iw79dSeLkDv3LmrdevWPTyeOjd024xtUnp4+fLlMj1X+4Lpf9rZpEkzd1EQP3v2AhdQK6ifM2e2G+tcoUKFLB9bY6hl8+aNUdtfsmQpdzJCx08nPIoygmYAAOBbKlrT4KCsf9gBxUW7JtVs+oLsjVNWYH14k8LNzsiYFhyLqmhL2bLRA69q1fadONu9e3eW96PCXxmpt3mfbHbRR9i+fZv7O2nSBHeJJvC/ISN6nGHDnnHjhFXITEWzdHnqqf+zTp262O233+1Sn3OqVKnS2W5rRiqs9vzzz7g08r1797h1NWse5E5O/PnnonTjsbNq/++/z3KXWLZt20bQDAAAAKDwdWxWw83DnJ1iYGVKJbk6APFAVbJl/fr1Ua/funVf8FaxYsUC3S/1JMudd97nKnPvjwLHwYOvcpclS/62yZMnuTHPSnHWuOYnn3zWCtKIEU/ZqFEf2vHH97C+ffeNn/Z6l087raelpmb9OlK6uwwceHm4UFhxRTUNAADgW+oJ2bpjr7vsr1cEKOo0fdSgXi1sfwMVdL228+t0UxkphVoVmZcsWWxbt27JdP1vv81wfw85pFGePN4/vc9Z88Yxq2hZRqr2PXz4E64XV9Rzu2/6p9nhccNKQX/xxf+4CuGR81Fn9/EPlHq8VQ37/vsftrZtDw8HzJo2SuOZ9/eRqorbXgXxjPR5/OKLz9q7775VLD6bCZoBAIBvpQVD9v2M5e6iZaC40xj/If0Ocz3Jkbw4TOuv6dc6XAsgXpx00im2a9cuF3iqWrXnt99+tS+++MwFnjkt5BWLpqSSvXuzHuesImQK5hUYaxxwpP/85xX74IP37M8/F7r/ayzxe++9ZW+88Wq6IHLbtq0u/Vxp0Tl9/ANVsmQJl6K9ffv28Drt57Bhj1kwGMzU06yx0yow5qldu44LtidM+Ml+/HFcum0//fRje+ut/9ivv84osJMAhYn0bAAA4Gslk+OjtwwoKO0aV7cnh1SxSbPX2Otf7StW1aZRNZe+rZTseOlhjnTBBZe4dObRoz9zBbgOP7y9rVu3zlXgVgGxu+9+IFNBrNyqXn1f2rp6ijWXcqzqz6riremjbrxxiF111SBXRbpWrVo2Z84fLlisX/9gu/TSwW7bZs2a2zHHHGfjxn3vpoQ6/PCOLgAdP/4HVyhr4MA7c/z4B0rTUymQHzToQjvyyKMtNTXFjbNevXqVS3XXWGTNs1yixL7x0ToxoSmuNM1Uly7dXNG1W265w6666jK7886b3boGDQ6xv/9e7MZ4q6jbtdcOteKAnmYAAOBbSYkBO/mIg91FywD2UWDcucU/0xhdfmpLNydzPAbM3tRKw4e/4MbOquDXqFEfuTmajzvuRDdH8uGHd8izxzrjjLOtY8fONm/eXPvww/dcD3csbdq0dY9/9NHH2syZ0+2jj953UzFpLuhnn33FKlWqFN5Wgf3llw9xPbjqif3qq9EuTfuJJ55x44pz8/gHQmOrBwzYF9R//PGHrpK5gl49z9oH9TZPnTo5vP0NN9ziKpCPHv2pO1kh9es3sFdeect69z7NzS/94Yf/dYF1r16n2ssvv2F169az4iAhVByS0ONEWlrQNm7cVz3Qb5R1kZycaCkpadmeH9BvaIM/xFMbxk5Zal9P/We+Q9FH5ubt+1KXKpUrETUlqUfHetazU33zs3g6DrHQBn+gDf5QFNpQVNqRmrrX1q9fZVWr1spWheMDsWdvml05bN+UTs8PPdpKlsi7gDm7U1v5GW0onm1ISdlrGzbs/z1YpUpZS8zmyVjSswEghl17Um3Ttn1TNETjBc/RbgcAQEGcyPXc/tKkuD2RC/gdQTMAxFC6ZJJVLl8yV7cDkDfSgkGbuWB9eI7axP/NiQoUN5zIBQoPv+wAIAadmY92dr4opA8C8ULvseXr9lV+jbdqwEBe4kQuUHh4FwEAAN8KJCTYYQ2rhpeB4irWiVwA+Y+gGQAA+FYgkGCN6lQs7N0AABRjDAwCAAAAACAGepoBAIBvqTqwV8hIYzOjVQcG/ImiF0BRee/R0wwAAHwrLRhy0+zoomXA7wKuwnuCpaWlFfauAMVSWlpqxHsxbxA0AwAAX0tMDLgLEA8SE5OsRIlk27lze7p5lAHkP73ndu7cYUlJJdx7Ma+Qng0AAHwrKTFgfbo2KOzdAHKkQoVKtn79Wtu0aZ2VKVP2fz/e42togUZCxHvMTxuKUxtCrodZAfPevbusYsW8naKQoBkAAADIQ2XKlLNKlYK2fftW27x5fWHvDlBsJCWVcAFz6dJl8/Z+rYh5++237V//+pfNmDHDypbN3pO1Z88ee+GFF2zMmDG2YsUKK1OmjLVv396uueYaa9GiRbptP/vsM7v55puj3s/hhx9u7733Xp60AwAAAPFLP9pLlSrrer+CwaDFW89gUlLAUlODcdvLSRuKXxsCAQ3lyZ/wtkgFzVOnTrX/+7//y9FtUlNTbcCAATZt2jRr1aqVXXDBBbZ27Vr7+uuvbcKECfb666+7YNgzf/5893fgwIFWunTpdPdVq1atPGoJAACQtGDQZv25wS23blTVEvOwsAtQEPQjPjHR4i7QSU5OtISEtLgO1mhD4UsoAm0oUkHz6NGj7c4777Tdu3fn6HajRo1yAXO/fv3soYceCk9loQD84osvtgceeMA++eST8PYLFiywcuXK2S233JLnbQAAAOnpR9aS1dvc8mENqxb27gAAiqG4D5o3btxod999t3377bdWp04dS0pKsiVLlmT79upRlmuvvTbd3I8dO3a0Tp062aRJk2z9+vVWrVq1cE9z48aN86ElAAAgo0BCgjU/uHJ4GQCAghb3OU4LFy607777zs444wzXI1yzZs0c3f60006zIUOG2EEHHZTpupIlS7q/O3fudH83b95sa9assSZNmuTR3gMAgKwEAgnWtH5ld9EyAAAFLe57muvXr2+ffvqpNW3aNFe37927d9T1CpCVtq1iYt5YZaVmi4o5qEjY9OnTbdeuXW7M83XXXWetW7c+gJYAAAAAAPwm7nuaFdDmNmDOyuOPP27bt293PdjJycnpioB9+OGHtmPHDjv99NPtyCOPdCnc559/vk2cODHP9wMAgOIsFArZnr1p7qJlAAAKmi97mrt37+7SoLOilGr19uaHl156yQXGGiOtsc6etLQ0t+6qq66yM888M7x+ypQpdskll9jtt99u33zzjZUoUSLXj+3X4Vrefvl1/7KDNvgDbfAH2uAPtGH/0oIh+2rKvlolvbs2sKR8SNHmOPhHUWgHbfAH2uAPCUWgDb4Nmnv06OHSo7OSH73L8txzz9nTTz9tlSpVcnM3V6hQIXydAmNdMlLBsD59+rgx1UrZ7tKlS64eW2O1qlTJ24m485Ze7fF+lp82+ANt8Afa4A+0ISvqXD6nR3O3XKZUUj7+8OI4+EdRaAdt8Afa4A8JvmxDZBHouAya77rrrgJ/TI1T/te//mXvvvuuC5hfe+21HBX8atGihQualy9ffkAHLicHr3D4ff+ygzb4A23wB9rgD7QhK+XL5j6DK2c4Dv5RFNpBG/yBNvhDgsWzuB/TnBdSU1PtxhtvdAGzqmjrb8uWLTNtN2/ePJs8eXLU+9izZ4/7eyCp2QAAAAAAfyFoNnPzPI8ZM8YaNGhg7733njVq1CjqdkOHDrWLLrrI1q5dm+k6pWVLq1at8n1/AQAAAAAFo9gHzZqu6uOPP3Y9zG+99ZbVrl075rY9e/Z0f5988sl0FTy/+uorGzdunB1xxBExA24AAAAAQPzx5Zjm/KLUalW6VuGuzp07u3XDhw8PFxZ7//33o96uf//+VrVqVRs4cKB99913LshetGiRdejQwRYvXuwC5ho1athDDz1UoO0BAAAAAOSvYhU0K2AeMWKEm65KQfPq1avDhbvGjx/vLrF6mBU0lytXzo13fvbZZ23s2LGuZ1pFwzT9lKamUuAMAAAAACg6EkKRecYAAAAAACCs2I9pBgAAAAAgFoJmAAAAAABiIGgGAAAAACAGgmYAAAAAAGIgaAYAAAAAIAaCZgAAAAAAYiBoBgAAAAAghqRYVwDRTJ482Z577jmbNWuWlShRwtq1a2dXXXWVtW7d2uLFlClTbMSIEfb7779bMBi0Fi1a2BVXXGFHH320xYOPP/7Ybr/99iy3qVOnjn3//ffmd1u3brVnn33Wvv76a9uwYYPb7969e9ugQYOsZMmS5ndpaWnuPbBnz56o16tdBx98sMWTjRs3Wq9evaxWrVrutRYvxo8fby+++KItWLDAEhMTrVOnTjZkyBBr2rSpxYtx48bZq6++arNnz3avrUMOOcTOO+88O/fccy0evf322/avf/3LZsyYYWXLljW/Sk1Ndfv6/vvv24oVK6xGjRrWt29fGzx4sCUnJ1s8WbNmjZ1yyil2ww032AUXXGDxZNWqVTZ8+HD76aefbPPmzValShU75phj7LrrrrOqVatavLTh6aeftl9++cU2bdpkjRo1sv79+9uZZ55p8Ui/NfQ98Pnnn1uTJk0sXtx111324YcfRr1Ov/f0/ogHn376qb311lu2cOFCK1++vB1++OFu3/Xd4GdNs/G9++abb1rnzp0tnhA0I9tGjx5tN910k/tB2rNnT6tUqZJ9++237gvhqaeeshNOOMH87ueff3Y/hBTwKzjTDyK16/LLL7fHH3/crfO75s2bu2AgVuCgkwHx8EGkgFmvnUWLFrkfRvoS0EkZ/WiaN2+ePfPMM+Z3f//9twuYu3Xr5oLnjCpWrGjx5uGHH3aBs4LmeDFq1Ci77bbbrHr16nb66ae7Y/LFF1+4IFQ/ONq2bWt+99///tfuvfdeq1y5sjtpoZNGP/zwg1un94h+BMaTqVOn2v/93/9ZPHjggQdcwNyxY0c7/vjjXZCvz6H58+e7v/Fix44dds0119j27dst3qxcudLOOussd/LU+z6YO3euOy4TJkxwAZCCaD9bu3ata4M+P3v06OE+Q/Wb484773TfafH2HtbzHk8nTiPpvavXi35jZNShQweLB08++aS98MIL1qBBA9cOnRD76quv3O+kkSNHWt26dc2vhsT4jar3yAcffOC+q3VCKe6EgGzYunVrqH379qGWLVuGZsyYEV6/adOmUM+ePUOdOnVyy3536qmnhlq0aBH6/fffw+uWLFkSateuXahr166h1NTUULxavHhxqE2bNqFTTjkltGvXrpDf3X///aEmTZqE3nvvvfC6tLS00OWXX+7WT506NeR3o0ePdvs6duzYUFEwbtw41x5d+vbtG4oHeq23bds21L1793SfQXPnznWfV2effXbI77Zs2eLa0K1bt9DatWvD67dv3x467bTT3PGYM2dOKF588cUX7rPIey2pHX41ffp0t4833HBDeF0wGAzdcsstbr3eE/Fg+fLl7j3rPedvvfVWKJ7ceOONbr9HjRqVbv1zzz3n1j/00EMhv7v99tvdvup7wZOSkhI677zzQk2bNg0tWrQoFC927twZOu6448Kvp/nz54fihd6/+jzVb4l49dtvv7nXzAUXXBDavXt3eP2XX37pjscdd9wRijfBYDA0YMCAULNmzUK//PJLKB4xphnZ8uOPP9q2bdvcWdTIHjX1Nl999dUuleqTTz4xP0tJSXFne5U20qpVq/D6+vXr2xFHHGHr1693qXnxSmlU6mF75JFHrFSpUuZnO3fudGewlUIbmXoaCARcuv8ZZ5zhUibj4Wy2NG7c2OKdeqnuu+++uBmm4FE6ts5aK41Zn0eeZs2aueOizAulOvuZeqP0nlBvgtriUUrzpZde6paVsup36mHT98HQoUNdL088DE1455133F/ttychIcG1QX/Vo+N3r7/+uvXp08d9v+m7LN5omNR3333nXi/KFIl02WWXuawL/QbxO/UE6veF0uM9SUlJdtJJJ6mDyn777TeLF8oeVK+/vqPjzdKlS93naTylk0f7XNLnz0MPPZRuqJqyPM855xz3uzXefPjhh+677vzzz4+LbMhoSM9Gtixfvtz9bdOmTcyxC9OnT7dLLrnE/Eqp2PoRqvQQBWT6MvOsW7fOBWzxmE4rStlRSqE+TONhfLlSN3ft2uVS2DLS/sdDG7ygWSco4iE42J9hw4a5k19KBz7uuOMsXui1orHjGekEklI+FUhrSImfNWzY0I1T6969e6brvB9M+hHodxp3p+BHJ710Ek+B6JIlS8zPpk2bFjVVsGbNmi4tUjUw/E5jA1UP4v7773dDRjSeNp7o+1jjlsuVK5fpOr13ddH3hd+pHkE0ixcvdn/jZVy2atboNXXzzTe7k5LxxjuZHc9Bs04SaShexuBYgbSGk8TjSfmnn37anUy9/vrrLV7R04xs0Rhgr7c2I/VASzz00iqoVIB89913u7PCKtShcSP6ktAPvXgMmtWLpoBHwdu1115r8cD7Ij700ENdj/Npp51mhx12mAvWNIbH7z2Dke046KCD3PhrnQFWG04++WT3g0M9C/FCJ1zeffdd98NVP77j2d69e13v8pVXXulOAqiGgd+pV1zFaVSUMCPVjYiXbAb9wFPhGmW7VKhQweLhtbJ69WqrV69e1Ov1XtB3xJYtW8zPFCwr00tFguL194VOuEcrljVp0iR3wkjfFfFEn//6jaHvM9UraNmypR155JHmd/qNpzHYCtguvvhii0fe7wv1OCt7R++LLl26uBN56jTxO/XwK2tHr3nVs1D2Xfv27d1F39Hx8Fs7o//85z8um1Pfy9FOjsULepqRLV468zfffONStCN5VZrjofiIzpyWLl3aVQCPLHChD9Y77rjD4pF6dtSbo/TUatWqWTzwvrjeeOMNd0b1xBNPdEV4lIKqkxj6olBhNj/T6109mfpxpEBBBYTUu6niU0qpUqqkimrFQ+CgH0n6UXfRRRdZPFOap4aPeKn9F154oa+zX7LTC6pChXpfx0PvvwofxVMBOZ1UkVgBvqrVeieG/XxC9aijjrKiaPfu3e4EjHfCO57o94T3G0MZC6rs7/eMF9F+/vnnn/bRRx/Fxf5m1dP8/PPPu2w2ZUgqNV7HY+LEia4QlTJJ/P77SJXY9Xtbrx+dUPrrr79cVqGyOpXqHC+ftXv37nXp5ipymTF+iDcEzcWc0gF1NnR/VfB0US+aqjNrCpGBAwe64FMVatVDpdTnwupZy24bVFVU1Wi1v0qTUpCjVBet0xeEUiT1I7uw5KQdkVQdWF9uOiaFLbtt8FLtFGC+8sor4TPwWq92aHoLVTJXFVW/tkH7p94/9VJ5Pf1eVXCNQ9VYSPU+F8YY4Zy8lnQCSWfktb8aouAXuXk/KAVMY+T1vtbJGL03dCJD6WxaFy/vaS/VWenNOhGgnkR93haGA2mD33knV7xMqoy89bGmlEP+9ngqjVO9hpqZI9pQHj/TsDVNnaiTpxrHqRPz6m3zc8VjnaxWz7hONEbLeokXet8q60WV79Vj7lEQrbHaOpmtVGG/8obiaGiIxvhrf70TGPpOe/DBB93JpHip7P/FF1+4nnN9RxTW91heIWgu5vRF5J1tz+rDXz84lYKqVEfNZ6mL6A2gN7R6qgqr+FR227Bs2bJw+ul7770Xnr7ixhtvdF8S+iBSb1thpbhltx2RlKajD1YFnbFSDP3YBqXIi05cRKas6fWkH0o6efHll18WStCc3TZoShQF9xmp10oZDUpt05dFYQTN2W2DzsjrpMWAAQNcirCf5Ob9oJ5BDb3weqk0lZx6Fbp27erS5uOhDfLHH3+4H9y6rcY6F+Z0frltQzzwvrOiDTvyekikTJkyBbpfxZ3eu/oe0AltfSc/9thjFm8iM1w0bdY999zjTt699NJL5kc6OacpsTTcKB5PgEV64oknoq7X94E6SJSdp9eYXwumeievFSgrpTyyx19FtJShp/eGTuZFFgnzq08++cS1Kd57mYWguZjLybyBSgXRfKjqHVT6joJOBT160+rMWGGlBme3DepR048jjQ+JnO/RC3L0Jaf2FVbQnJs5HPXBKYUREBxIG3799Vf3Vz+IMvLODKv3szDkxVyaXru8Anp+bIPGjSvlUe/ryMrBfnGgx0E/iFQB+eyzz3Y/kgrjPZKbNih9UD236jVXjQKNdS5M8Ta3bE5obJ1+zHl1OTLy1ntp2sh/Gj+u4GbmzJluWJiKa8XzGEjR56wqnGv4kU7ExMpsKExKn9Vz/tprr8V9b2Aseq/r5LC+l5U949cCnt7njTp4ImeE8NrgdQKpHoNf2+BR5p3SyfW72s8p8dlF0IwcUcVp9XpE9nx41UX9PlG5PmBEadgZeUV2NEY1nugEhs5CFmZPVG5ojE6sHh5vnd+/uPWlq7Hket1nrIqqs9ji57PAGi+lglmxquKrt1NfzppyRClhfqUfQLNnz3ZfyjVq1Eh3Xe3atd1fFXOKB2PHjnWZL0obVvZOvI8x9zsFL3qNxDq5pfWqrB3vQVu8UAaShrZoaII+d5ROGy/PvT7zNSuEvrc6dOiQ6Xq9zjQmVScFIqeV8wtvBgJlHUWjKc1EJyD9nGKu3lelxOu9HZmaHU/fzcoa1O+6WBkw3rASv/aUR9LQBO2vhqoVBQTNyHaAoEIEvXr1sttuuy3qh63fi5F4gY2m5ND47EjetCh+/DLLiqp+q8JixrORfqcqkKKpUZQyH0kBUDxMF/HZZ5+5YmXqzVTPSCSdWZXI+cD9RhkW6tGMZsSIEe69oB4Sv1fTVnHCRx991G655ZZM4/r140n8MHQhOz3MCpiVJql0VFWUR8F8FqmQn3puIl8n+s7Td4WKFKJgCivq/auAWRlsGnvqxx7ZWJRtpzml9X2sYTkZs3o0XlhTXqoYkh/17ds36pzMqt6vz1EVGtVvKL9XxVd2iLKLdGJeJyEjqWbKnDlz3Heb0tD9SgG9fjuoeJl+m0b2JisA1fHQ6ygefq/++r+swmgnkuKRf6q+wNeUVqFeZgUKkePbFLRpzKB6pKLNMeonOtOlsdk6ex3Z86QPUlVs9lOac3aoF0RnraOlOPudemf1IaqpjiJ/YHhz+SkFye9Bg35M62ywppfyxmh7lS/1elJxPH15+5V+/GjsWrSLqNdWy5qKzc805lbPtYrsaKoOj97j//73v92y39ugz9SbbrrJ9SyowIvfX/tFiQrtiIr5ecUs9Vf/Fz+/h4sSve5VY0En31XgKJ4CZtGQL9VOUNCvMZyR1B5luul9rd9RfqTPyGjfBV5vrQqZ6f9+D5o1TFC/LXTCK3KGFL2nNdZZBal0AsDvvM8d1QzyepZF6fPea8lPhTtjmTt3rvt+jocpE7PDn+9e+HYKBfVM6cNVP1T1o1TFmpQioh+nhVGdNic0lkVzxGlssyofqw360FGKswJQfUgVRtGm3FLPiMTrOBFVYVdRC40n1xlhja3VsdCZVZ2xj5Za5Sc6k63x8SqQp9S1k046yX25KX1NX8yqeKwKnshf6glXD616m/W+1nFQz46mwtPJDBUUat26tfmZCisq4NeJCo3l12sqI02lFQ/zvMYbBTqnnHKKjRkzxg3PUW+bMkV0UWaV308GFwX6LvMCHPX268R2Rvqdoe8FP1MRQlXvVzaeMmD0+a9xwrro5LY+p5D/VHRNvy30m1U95ToOmr5Pw5H0/vb760j69evnatZo/5UFoM9+ZStoVgj99oiVJeY3S5cudRkKCpyLAoJm5KhnTZUfFXSqd1mpRuq91ZvX78UIPEoFVtqvegd1Nlg/rpVOpfTaeOtR8HrL/X7mNxaNLdc0RwoQNJWZ0vNUkVpzHGsoQDzQa///27sXIBvrMI7jf6X7/X6bropCKEpLKUrKdDVKSdKILlKI0W3aSoyhVBjV6F47Nd2kQjdy2+hKUks3aaOEXFpCeJvvM/N/5z3vnrN7zl7s2fX7zOws7zn7nv972eF5n+f5/zkOJnlhEjkewlBWxSzz2d6uUJPQB0mfHZMG8Z9vrgNLpvDgglLPbMd/6HyVAqXxydBrqKC5clAOT/ULv8P8LtN/ysOWbFjGb3tAxRFtCWBJyGRoQcr2YId/v/g3jWopJv3i3zXuJR7W82+CZmHfNqh8ZJZsMvy0vRBo8nCViRW5h6pDFQNJKO4jHqiyJjPfKckm489xVJfJCVevXl2jkge1gqpaXFdEREREREQky2V/QbyIiIiIiIhIFVHQLCIiIiIiIpKCgmYRERERERGRFBQ0i4iIiIiIiKSgoFlEREREREQkBQXNIiIiIiIiIikoaBYRERERERFJQUGziIhIDRMEgctW2Ty2qlDdz0d1H7+ISDpqp/UuERGRFEaNGuVGjx6d9vtPP/10d+utt7quXbu6c845xz311FMuG/3+++/u3HPPTfn67rvv7vbdd1938sknu+7du7vGjRuX6/PatGnjlixZ4mbNmuX233//Mu1jzZo1bsSIEe7MM890bdu2DbfXq1fPxjtnzhxXlb755hs3bNgwl5eXV6H7vfPOO924cePSfv/ll1/uhg4d6qrSsmXL3JAhQ1yPHj1cw4YNXXVT3ccvIpIJBc0iIlIuBGQXX3xxsYCTAO3II490TZo0SXitTp06rjoh2EwWPK9cudItWLDAffDBB+7jjz92Tz75pGvVqpWrSgSCb731lsvJyXHZqFOnTm633Xar8P2ecsopbvPmzQnbFi5c6H744QdXt25du0fj769qAwYMcJ999pm74YYbXHVU3ccvIpIJBc0iIlIu559/vn1FEbgRNDdr1ixpRu/ff/91EydOdHvssYfLdvvtt597+OGHk77Gcdx3333unXfecQ899JAF0LVq1XLZVirLud5hhx1qbCkvwThf8QoIgmbuzd69e7tsU93Lmqv7+EVEMlH1/4KKiMh2h2wjGedDDz3UVffjyM3NdTvuuKNbvHix+/XXX1024lwfe+yxVT0MERGRaklBs4iIbHOUdVIye+ONNyZkBtnGa+PHj3eXXnqpa9SokfU9P/74427Lli1u3bp1ltGlZ/fUU091V111lfvyyy+TfgYl09dee61r2rSplYh37NjRvfHGGxWeIdtzzz3d3nvvHfYURzFext6uXTvrfT7jjDPcbbfdZqXD6frqq69cnz59rPSb3lGOu0OHDu6FF15wW7duDd/HufN9vbfffnt4Lv1rviT5p59+SlpS7xUUFNjr11xzTcL2L774wvXs2dN60rku/Pxzzz3n/vvvv1KPgcoDXyK9fv16+zM93FGzZ88O98+5uuCCC+zcFRUVucrgx/Tqq6/aPcX5Oe200xL68wsLC91dd93lzjrrLDv3jJn3/v3330n3OXXqVHfTTTe5li1b2vuptOjcubN7++23E1oX+NzPP//c/s59yd/Z7l+jR3vRokWuV69etg++2C/jwXvvvWe/H/TRc2/RGsDvR1y64/ef279/f/sZ7rfmzZvb/q+88kqroEhn/CIiNZXKs0VEJKuMHTvWzZgxw4IYgkwCvzFjxliwRQD522+/2WvLly+3EvBu3bpZUHL88ceH+3j00UctkNh1110twCOw5T/599xzj/v6669tAqOKMm/ePLdq1SpXu3bthGwu25jsjBJhMuoELgQrH374oQVXHBPBf2mB3d13322ZbIJlgv+lS5e6b7/91n333XcW4Nx77732XoLYuXPn2jaCrMMOO8wdeOCBxfbJeTrppJMsOP7555+L9ZhTyu335xFY3n///TYOAlr2y7Wg9H7mzJk2mRvHn8pRRx1l+3v33XftfRdeeGHCZGfPP/+87YsSch5yUBLPdeIccb5eeumlMk+OVhoCf84p14LAz58PrisTvK1du9b6ojn3P/74o41lypQp7uWXX3aHH354uB+CbR78cM9xDLQeUHnAeeJr9erVdq/SI8+5+PTTT60vnoch++yzj23nHgc/d8UVV9h9S/DKtfrkk0/s89n+2GOP2XjoXc/Pz7f7nVaBvn37huPJdPxgIjqCYK4zv2OMj8nbeNDDZ7Rv377E8YuI1FiBiIhIBXvzzTeDunXrBgMHDkz6+uzZs+31nj17httGjhxp2/iaOHFiuH3q1Knh9vbt2wfLly8PX+vfv79tHz58eLht2rRptq1du3bB4sWLw+0rV64MOnToYK+NHz++1GMoLCy097Zu3brYa5s3bw5WrFgRTJgwIWjVqpW9b9CgQQnv6du3r23Pzc0NNm7cGG6fPn160LBhw6B58+bB2rVrw+18Du9nnFi/fn3QtGnToEmTJkFBQUHCvv05adSoUbBp06ZwO+eb7ZMmTUp4P9vYj/f000/btlGjRhU7tjZt2gQNGjQIVq1aZX9fuHCh/T0nJyeYP39++L5169bZ9WM/Y8aMKfV8JhsH5s2bF9SrVy9o1qxZMGfOnHD7hg0bgn79+tnP3HLLLUGm/P3E95LuUb64H70tW7bY9eJ6MK5x48aFr23dutXOGT/TrVu3cPuSJUuC+vXrBy1btgyWLl2a8Dl5eXn2/rZt2yZs79Kli23n+OP3HF+9evUKry3nmuvCdsY0efLk8Gfy8/Nte4sWLcJtmY4/+rmc66KiovA1ri3bO3bsWOr4RURqKpVni4hIVqE8l0ykd/bZZ4dZLJaqimZP/QRkZJ+jWUuQGSXD6ZGppDQVlDani+wb5afRr/r167sWLVpYZm/FihVWBj5w4MDwZ/788083adIkd/TRR1t2e+eddw5fI+NMWTmZaCYQS4X9cuyULJ944okJr7Gd7PWGDRtsP5m66KKLLKsbLbv12UmyrWRdWU4LZCYpwb7jjjtcgwYNwvdyTTifHBvviZaKZ4KMJyXzZDOjM63vsssubvDgwe6ggw6yUvvoNa5IJ5xwgmVzPX9euO6UQF922WXha0zyRsk0mXoyrWRuQcb1vPPOs7J4MvxRZG7ZJ9nsTFBWvdNOO4Xn2s/MzvdoaTv3IRlp7hfaAZDp+KOY2C46QZ8v06esX0Rke6WgWUREsgrl1HE+gIsHj3vttZd937hxo32nr5NSWEqAKZGNI1g44IAD3Pfffx8GGKXx5ah8EWxSnuwDEHqyp02bZiXSPsDx/b8EkZRUR7d7vizb94Umw3JdjzzyiLv55pvDbSyrREk15ej+mNPpKY475JBDrH+X0nH2Fy/NvuSSS8JtfozRwNIjoOWaEDT+8ssvrix8T3p8BnZQ6ty6deuE91U0SpfjSjpmrjs9y9H3cU/Qf03ptLdp0ybrXafEnnLnTK4T9+gRRxyRsI2S9WS/A/A99f6eyHT80fuCr/i+eTBC+beIyPZKPc0iIpJV6I+M88s4xV+LL+9E3yjZVzDxUUnoiU5nyatkS07RZ0uWmX5YsqPxSa3INIOJufzkXMksW7asxM8mAzt58mQLkgnAyFb69Yj9sZd1YjMeAtAvTkacDD774c88JIgejz+WZGtVx48l2leeLq4DDxbiwZrng0cyqZXBB5xR/pjJ9vKVyl9//RX+maCYCbo4h2Rw2UdZs+/JxpTqdyD6WnnGH30IFcdDKB4CiIhsrxQ0i4hIViErV1Z+BmGC4XggGxctmc4UWdF+/fq5YcOG2Xdm5Y4GjD5YIitI+W8q8Wxi/FiYMXn69OlWqkw2kwwh5eGUsFPOXJ6SWWZdfvDBB937779vQTMZeoItSnrJ8EbHQVBGlr0kyYK5dJQW9PtzWZ7rVZJk61f7z6QiwGd4kznuuOPsO1ULlOgzORv3HtUSPGTg+jOZHecuk0xtSZOqpSPT8XtVuca4iEg2U9AsIiI1BmXcZC0JxOLZ4Yp2/fXXu48++shm8GaJoNdeey0MwChbBiXi9IiWBf3OBMzMYvzEE08UC36YFbk8yGaynBdZc0qrCZ4RX4rq4IMPtv5YZvGujBms2T991GSqk2Wb/VJGlTV7djL++lFuzdJXpaHigICZvmbuO9bv9sjQbuvS5kzHLyIiJVNPs4iI1BhkI8nIsnRPsh5YAjMyrD169Ch3uSkBMplasoIsAcWyTJ7vp541a1bSXlYmv/LrHKfCUj/o1KlTsYCZ8l9fWhvN1GaaKfQBMktg8QCAXlomloqiLxsE8HGUwpOZ7tKli/U1l4U/VwTvcfTo0jMOltHaVvyYkh0zWM+YCb64vtFrdd111yUEzGDCLa+s5dqVPX4RESmZgmYREalRKJMFk3MtWrQo3E62j5msWQOXILQiyn2ZRIqAEaydyzrMYNZsZjkmg8sM0NEAnYwkk0YxCVeySZ08ZscGQWM02GIWaUrCPT/5E/wx/fPPP2mNn0wzfazMJk5pNuvwxsvjOT6C8eHDh9vs2h691YMGDXILFiywwJ2AuzSUmTPe6Plg/zyAGDlyZBh8gvdwDellZjKw+JrClYnzwPEwiVe8J51SfNabZgI13zfvrxVrKUfNnz/f5ebmJr1WnAsUFRVV+fjLojLHLyKSbVSeLSIiNQoBAxNckfllFmj6S+m3pYyaoJbeY0qNK0rv3r3dhAkTbEIrSnOHDBli21mOiYDwlVdesSWTCFDofaV3mD5hst05OTkp98tSQc8++6xNLFVQUGC9zGRzOQ6CTJbTIoDmc30/9THHHGPfmXV7xowZVkJOeXcqBNlk3gmkkpVmg4nOCNLZJ0tlcRyUVBMQ/vHHH1YKPHTo0LTOFQ8TeFjAfurUqWOBONeHzCf94VdffbVlSXmowXGSTacnnOB8W6Iv2c9cTun92LFjrf+3sLDQHhJQXTBixIhw4qzOnTtbcMr1IrPMcXJueMjAclCUnVPlwAMAZkX314prxL1IdcSAAQMqrKc40/GXRbLx+2MTEalplGkWEZEa54EHHrCggICPIIFAhswba9QSTPslrCoCQREBA8jszZ071/5MoEQwyjrLBDH5+fk2cRdB4ejRoy1QLAnZy7y8PJtQimB7ypQpFvTQo8p+CYjj2U2CUSadIktLQEOAWhofKBOEN27cOOl7OIZnnnnGSrfJ1FP2y2RhXbt2tWAx3WCJ4JfsOuOaOXOmW7NmjW3v3r27BZw8ROB6kV3nQUefPn2sV9z36G5LjIXrycMLMveUsNNHzkOG119/PVwKyy9l9uKLL9rPkLHnWvGAg55iZj7355jtHgEt1Qiss829UdYluypi/GVR2eMXEckmtYKyrlUhIiIiIiIiUsMp0ywiIiIiIiKSgoJmERERERERkRQUNIuIiIiIiIikoKBZREREREREJAUFzSIiIiIiIiIpKGgWERERERERSUFBs4iIiIiIiEgKCppFREREREREUlDQLCIiIiIiIpKCgmYRERERERGRFBQ0i4iIiIiIiKSgoFlEREREREQkBQXNIiIiIiIiIi65/wHmfsFpx3/7ywAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(4): 3.377  p-value: 0.497\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(4): 1.062  p-value: 0.900\n",
      "\n",
      "--- Running Placebo Test for Tanzania - cc ---\n",
      "Treatment cohorts: {2013.0: np.int64(279), 2015.0: np.int64(188), 2020.0: np.int64(72)}\n",
      "\n",
      "Testing placebo treatment at t=2011 for cohort actually treated at t=2013.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2015.0\n",
      "  Coefficient: 0.1847 (SE: 0.1420)  p=0.1971  ✅ PASS\n",
      "\n",
      "Testing placebo treatment at t=2015 for cohort actually treated at t=2020.0\n",
      "  Coefficient: -0.2665 (SE: 0.2777)  p=0.3401  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 2/2 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2015.0          2013  0.184693  0.141998  0.197103         61        131   \n",
      "1  2020.0          2015 -0.266506  0.277706  0.340078         28        131   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "1  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "============================================================\n",
      "********** NOW RUNNING: Nigeria **********\n",
      "============================================================\n",
      "\n",
      "Nigeria data loaded:\n",
      "  Observations: 12844\n",
      "  Time periods: [np.int64(2011), np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Number of households: 6375\n",
      "  'cc' variable found with 11618 non-missing values\n",
      "  Note: For Nigeria, analysis is restricted to start from the second wave: 2013.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 1: Treatment defined by 'cci' *****\n",
      "***** (All households: Never sellers vs Late sellers) *****\n",
      "**************************************************\n",
      "\n",
      "  Filtering to households present at baseline year 2013\n",
      "  Households with baseline data: 2997\n",
      "\n",
      "  Treatment assignment for cci:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2013\n",
      "  Total households in data: 2997\n",
      "  Control group: 818 households\n",
      "  Treatment group: 684 households\n",
      "  Excluded: 1495 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 1502\n",
      "  Total observations at baseline: 1502\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 818 households\n",
      "    Treatment (1): 684 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 818\n",
      "  Treatment households: 684\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 1085/1502 (72.2%)\n",
      "  Mean SMD: 0.039\n",
      "  Max SMD: 0.102\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 1389/1502 (92.5%)\n",
      "  Mean SMD: 0.051\n",
      "  Max SMD: 0.150\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 1478/1502 (98.4%)\n",
      "  Mean SMD: 0.057\n",
      "  Max SMD: 0.167\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 1087/1502 (72.4%)\n",
      "  Mean SMD: 0.060\n",
      "  Max SMD: 0.158\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 1367/1502 (91.0%)\n",
      "  Mean SMD: 0.060\n",
      "  Max SMD: 0.176\n",
      "\n",
      "kernel:\n",
      "  Matched: 1502/1502 (100.0%)\n",
      "  Mean SMD: 0.065\n",
      "  Max SMD: 0.174\n",
      "\n",
      "radius:\n",
      "  Matched: 1500/1502 (99.9%)\n",
      "  Mean SMD: 0.062\n",
      "  Max SMD: 0.152\n",
      "\n",
      "stratification:\n",
      "  Matched: 1502/1502 (100.0%)\n",
      "  Mean SMD: 0.065\n",
      "  Max SMD: 0.174\n",
      "\n",
      "Selected method: nearest_neighbor_1\n",
      "\n",
      "--- Nigeria: DID Results (from 'cci' analysis) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Nigeria - CCI (never sellers vs late sellers)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               748    0   748\n",
      "1.0               754  862  1616\n",
      "All              1502  862  2364\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.231775, unique_values=2\n",
      "  treatment_group: variance=0.216387, unique_values=2\n",
      "  post: variance=0.231775, unique_values=2\n",
      "  emp: variance=0.248827, unique_values=2\n",
      "  lognf: variance=31.596960, unique_values=1256\n",
      "  educh: variance=0.236764, unique_values=2\n",
      "  head_age: variance=215.538025, unique_values=80\n",
      "  head_sex: variance=0.095894, unique_values=2\n",
      "  dependency_ratio: variance=0.776824, unique_values=82\n",
      "  vharvest: variance=45667237888.000000, unique_values=996\n",
      "  farmsize: variance=4.601635, unique_values=2281\n",
      "  asset: variance=3.441792, unique_values=1010\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 2364\n",
      "  Unique households: 1085\n",
      "  Unique time periods: 3\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 748 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 754 obs\n",
      "     Treatment=1, Post=1: 862 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 2363/2364 (100.0%)\n",
      "  Mean: 0.536, Std: 0.499\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + lognf + educh + head_age + head_sex + dependency_ratio + vharvest + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0428\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -2.4470\n",
      "No. Observations:                1932   R-squared (Within):               0.0606\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -1.6581\n",
      "Time:                        15:47:47   Log-likelihood                   -255.36\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      4.6604\n",
      "Entities:                         984   P-value                           0.0000\n",
      "Avg Obs:                       1.9634   Distribution:                   F(9,937)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             39.880\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                   F(9,937)\n",
      "Avg Obs:                       644.00                                           \n",
      "Min Obs:                       242.00                                           \n",
      "Max Obs:                       880.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.1205     0.0577     2.0870     0.0372      0.0072      0.2337\n",
      "treatment_group      1.4371     0.8791     1.6348     0.1024     -0.2881      3.1623\n",
      "lognf                0.0160     0.0047     3.4112     0.0007      0.0068      0.0252\n",
      "educh                0.0535     0.0553     0.9663     0.3341     -0.0551      0.1620\n",
      "head_age            -0.0018     0.0060    -0.2898     0.7720     -0.0136      0.0101\n",
      "head_sex            -0.6854     0.5843    -1.1730     0.2411     -1.8321      0.4613\n",
      "dependency_ratio     0.0092     0.0278     0.3317     0.7402     -0.0453      0.0638\n",
      "vharvest         -5.988e-08  1.089e-07    -0.5499     0.5825  -2.736e-07   1.538e-07\n",
      "farmsize             0.0007     0.0103     0.0685     0.9454     -0.0196      0.0210\n",
      "asset               -0.0078     0.0267    -0.2930     0.7696     -0.0601      0.0445\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.6493\n",
      "P-value: 0.0000\n",
      "Distribution: F(985,937)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Nigeria - cci ---\n",
      "Event times for treated units: [-6.0, -3.0, 0.0, 3.0]\n",
      "Omitted period (reference): -3.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m6.0 + treat_event_0.0 + treat_event_3.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0048\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0744\n",
      "No. Observations:                2363   R-squared (Within):               0.0407\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.0749\n",
      "Time:                        15:47:47   Log-likelihood                   -547.18\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      2.0579\n",
      "Entities:                        1085   P-value                           0.1040\n",
      "Avg Obs:                       2.1779   Distribution:                  F(3,1273)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             1.1175\n",
      "                                        P-value                           0.3408\n",
      "Time periods:                       3   Distribution:                  F(3,1273)\n",
      "Avg Obs:                       787.67                                           \n",
      "Min Obs:                       322.00                                           \n",
      "Max Obs:                       1085.0                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m6.0    -0.0063     0.0843    -0.0744     0.9407     -0.1717      0.1592\n",
      "treat_event_0.0      0.0884     0.0510     1.7348     0.0830     -0.0116      0.1884\n",
      "treat_event_3.0      0.1066     0.1152     0.9254     0.3549     -0.1194      0.3326\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.8737\n",
      "P-value: 0.0000\n",
      "Distribution: F(1086,1273)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Nigeria_cci.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(1): 0.006  p-value: 0.941\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(2): 3.011  p-value: 0.222\n",
      "\n",
      "--- Running Placebo Test for Nigeria - cci ---\n",
      "Treatment cohorts: {2016.0: np.int64(1386), 2019.0: np.int64(230)}\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: -0.0100 (SE: 0.0660)  p=0.8793  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year     coef        se      pval  n_treated  n_control  \\\n",
      "0  2019.0          2016 -0.01003  0.066016  0.879322         80        401   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 2: Treatment defined by 'cash_sale' *****\n",
      "***** (Among CCI sellers: Never cash vs Late cash) *****\n",
      "**************************************************\n",
      "  Restricted to CCI sellers: 3791 households\n",
      "\n",
      "  Filtering to households present at baseline year 2013\n",
      "  Households with baseline data: 2179\n",
      "  Note: This analysis is restricted to CCI sellers\n",
      "\n",
      "  Treatment assignment for cash_sale (among CCI sellers):\n",
      "  Control: CCI sellers who never sell cash crops\n",
      "  Treatment: CCI sellers who start selling cash crops after baseline\n",
      "  Baseline year: 2013\n",
      "  Total households in data: 2179\n",
      "  Control group: 973 households\n",
      "  Treatment group: 552 households\n",
      "  Excluded: 654 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 1525\n",
      "  Total observations at baseline: 1525\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 973 households\n",
      "    Treatment (1): 552 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 973\n",
      "  Treatment households: 552\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 918/1525 (60.2%)\n",
      "  Mean SMD: 0.035\n",
      "  Max SMD: 0.123\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 1292/1525 (84.7%)\n",
      "  Mean SMD: 0.047\n",
      "  Max SMD: 0.114\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 1426/1525 (93.5%)\n",
      "  Mean SMD: 0.064\n",
      "  Max SMD: 0.133\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 932/1525 (61.1%)\n",
      "  Mean SMD: 0.031\n",
      "  Max SMD: 0.061\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 1259/1525 (82.6%)\n",
      "  Mean SMD: 0.049\n",
      "  Max SMD: 0.105\n",
      "\n",
      "kernel:\n",
      "  Matched: 1525/1525 (100.0%)\n",
      "  Mean SMD: 0.086\n",
      "  Max SMD: 0.184\n",
      "\n",
      "radius:\n",
      "  Matched: 1525/1525 (100.0%)\n",
      "  Mean SMD: 0.086\n",
      "  Max SMD: 0.184\n",
      "\n",
      "stratification:\n",
      "  Matched: 1525/1525 (100.0%)\n",
      "  Mean SMD: 0.086\n",
      "  Max SMD: 0.184\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Nigeria: DID Results (from 'cash_sale' analysis among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Nigeria - Cash Sale (CCI sellers: never cash vs late cash)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post                0    1   All\n",
      "treatment_group                 \n",
      "0.0               827    0   827\n",
      "1.0               663  706  1369\n",
      "All              1490  706  2196\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.218235, unique_values=2\n",
      "  treatment_group: variance=0.234878, unique_values=2\n",
      "  post: variance=0.218235, unique_values=2\n",
      "  emp: variance=0.243935, unique_values=2\n",
      "  vharvest: variance=98008260608.000000, unique_values=1118\n",
      "  lognf: variance=32.352894, unique_values=1141\n",
      "  educh: variance=0.239531, unique_values=3\n",
      "  head_age: variance=197.910355, unique_values=76\n",
      "  head_sex: variance=0.095294, unique_values=2\n",
      "  dependency_ratio: variance=0.588117, unique_values=76\n",
      "  farmsize: variance=3.994326, unique_values=2154\n",
      "  asset: variance=2.479650, unique_values=907\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 2196\n",
      "  Unique households: 932\n",
      "  Unique time periods: 3\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 827 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 663 obs\n",
      "     Treatment=1, Post=1: 706 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 2195/2196 (100.0%)\n",
      "  Mean: 0.579, Std: 0.494\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0359\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -4.1180\n",
      "No. Observations:                1930   R-squared (Within):               0.0361\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -2.6764\n",
      "Time:                        15:47:49   Log-likelihood                   -357.82\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      4.2352\n",
      "Entities:                         894   P-value                           0.0000\n",
      "Avg Obs:                       2.1588   Distribution:                  F(9,1025)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             37.957\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                  F(9,1025)\n",
      "Avg Obs:                       643.33                                           \n",
      "Min Obs:                       293.00                                           \n",
      "Max Obs:                       840.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0779     0.0493     1.5801     0.1144     -0.0188      0.1747\n",
      "treatment_group      1.6924     0.6875     2.4618     0.0140      0.3434      3.0415\n",
      "vharvest           -7.7e-08  5.293e-08    -1.4549     0.1460  -1.809e-07   2.685e-08\n",
      "lognf                0.0113     0.0041     2.7554     0.0060      0.0033      0.0194\n",
      "educh                0.0727     0.0518     1.4035     0.1608     -0.0290      0.1744\n",
      "head_age            -0.0020     0.0052    -0.3772     0.7061     -0.0122      0.0083\n",
      "head_sex            -0.6542     0.3603    -1.8158     0.0697     -1.3611      0.0528\n",
      "dependency_ratio     0.0411     0.0310     1.3241     0.1858     -0.0198      0.1019\n",
      "farmsize             0.0157     0.0089     1.7679     0.0774     -0.0017      0.0331\n",
      "asset               -0.0024     0.0231    -0.1048     0.9166     -0.0477      0.0428\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.6942\n",
      "P-value: 0.0000\n",
      "Distribution: F(895,1025)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Nigeria - cash_sale ---\n",
      "Event times for treated units: [-6.0, -3.0, 0.0, 3.0]\n",
      "Omitted period (reference): -3.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m6.0 + treat_event_0.0 + treat_event_3.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0076\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0727\n",
      "No. Observations:                2195   R-squared (Within):               0.0313\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.0691\n",
      "Time:                        15:47:49   Log-likelihood                   -560.34\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      3.1955\n",
      "Entities:                         932   P-value                           0.0228\n",
      "Avg Obs:                       2.3552   Distribution:                  F(3,1258)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             1.8120\n",
      "                                        P-value                           0.1431\n",
      "Time periods:                       3   Distribution:                  F(3,1258)\n",
      "Avg Obs:                       731.67                                           \n",
      "Min Obs:                       364.00                                           \n",
      "Max Obs:                       932.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m6.0    -0.0317     0.0695    -0.4562     0.6483     -0.1680      0.1046\n",
      "treat_event_0.0      0.1049     0.0456     2.3010     0.0216      0.0155      0.1943\n",
      "treat_event_3.0      0.1017     0.0991     1.0259     0.3052     -0.0928      0.2961\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.9590\n",
      "P-value: 0.0000\n",
      "Distribution: F(933,1258)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Nigeria_cash_sale.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(1): 0.208  p-value: 0.648\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(2): 5.412  p-value: 0.067\n",
      "\n",
      "--- Running Placebo Test for Nigeria - cash_sale ---\n",
      "Treatment cohorts: {2016.0: np.int64(1032), 2019.0: np.int64(337)}\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: 0.0254 (SE: 0.0606)  p=0.6754  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year      coef        se      pval  n_treated  n_control  \\\n",
      "0  2019.0          2016  0.025401  0.060618  0.675389        113        380   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "**************************************************\n",
      "***** Analysis 3: Treatment defined by 'cc' *****\n",
      "***** (Among CCI sellers: Never certified vs Late certified) *****\n",
      "**************************************************\n",
      "  Filtered to CCI sellers only: 3791 households\n",
      "\n",
      "  Filtering to households present at baseline year 2013\n",
      "  Households with baseline data: 1495\n",
      "\n",
      "  Treatment assignment for cc:\n",
      "  Standard analysis (never-adopters vs late-adopters)\n",
      "  Baseline year: 2013\n",
      "  Total households in data: 1495\n",
      "  Control group: 561 households\n",
      "  Treatment group: 263 households\n",
      "  Excluded: 671 households\n",
      "\n",
      "  Baseline data check:\n",
      "  Total households at baseline: 824\n",
      "  Total observations at baseline: 824\n",
      "  Treatment group distribution at baseline:\n",
      "    Control (0): 561 households\n",
      "    Treatment (1): 263 households\n",
      "\n",
      "  Unique treatment values found: [0. 1.]\n",
      "  Control households: 561\n",
      "  Treatment households: 263\n",
      "\n",
      "Trying different matching methods...\n",
      "\n",
      "nearest_neighbor_1:\n",
      "  Matched: 446/824 (54.1%)\n",
      "  Mean SMD: 0.062\n",
      "  Max SMD: 0.139\n",
      "\n",
      "nearest_neighbor_3:\n",
      "  Matched: 652/824 (79.1%)\n",
      "  Mean SMD: 0.053\n",
      "  Max SMD: 0.128\n",
      "\n",
      "nearest_neighbor_5:\n",
      "  Matched: 756/824 (91.7%)\n",
      "  Mean SMD: 0.055\n",
      "  Max SMD: 0.131\n",
      "\n",
      "mahalanobis_1:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 462/824 (56.1%)\n",
      "  Mean SMD: 0.043\n",
      "  Max SMD: 0.134\n",
      "\n",
      "mahalanobis_3:\n",
      "  Performing Mahalanobis Distance Matching...\n",
      "  Matched: 652/824 (79.1%)\n",
      "  Mean SMD: 0.061\n",
      "  Max SMD: 0.147\n",
      "\n",
      "kernel:\n",
      "  Matched: 824/824 (100.0%)\n",
      "  Mean SMD: 0.076\n",
      "  Max SMD: 0.193\n",
      "\n",
      "radius:\n",
      "  Matched: 824/824 (100.0%)\n",
      "  Mean SMD: 0.076\n",
      "  Max SMD: 0.193\n",
      "\n",
      "stratification:\n",
      "  Matched: 824/824 (100.0%)\n",
      "  Mean SMD: 0.076\n",
      "  Max SMD: 0.193\n",
      "\n",
      "Selected method: mahalanobis_1\n",
      "\n",
      "--- Nigeria: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\n",
      "\n",
      "============================================================\n",
      "ENHANCED DEBUGGING - Nigeria - CC (CCI sellers: never certified vs late certified)\n",
      "============================================================\n",
      "\n",
      "1. Group-Time Combinations:\n",
      "post               0    1  All\n",
      "treatment_group               \n",
      "0.0              334    0  334\n",
      "1.0              291  309  600\n",
      "All              625  309  934\n",
      "  ⚠️  WARNING: No observations for treatment=0, post=1\n",
      "\n",
      "2. Panel Structure:\n",
      "\n",
      "3. Time Periods by Treatment Group:\n",
      "  Treatment=0: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "  Treatment=1: [np.int64(2013), np.int64(2016), np.int64(2019)]\n",
      "\n",
      "4. Variable Variance Check:\n",
      "  treat_post: variance=0.221621, unique_values=2\n",
      "  treatment_group: variance=0.229969, unique_values=2\n",
      "  post: variance=0.221621, unique_values=2\n",
      "  emp: variance=0.244153, unique_values=2\n",
      "  vharvest: variance=173267402752.000000, unique_values=655\n",
      "  lognf: variance=33.191334, unique_values=555\n",
      "  educh: variance=0.244296, unique_values=3\n",
      "  head_age: variance=204.630203, unique_values=69\n",
      "  head_sex: variance=0.086178, unique_values=2\n",
      "  dependency_ratio: variance=0.622207, unique_values=61\n",
      "  farmsize: variance=5.733744, unique_values=923\n",
      "  asset: variance=2.046103, unique_values=442\n",
      "\n",
      "5. Checking for Perfect Multicollinearity:\n",
      "  ⚠️  High correlation (1.000) between post and treat_post\n",
      "\n",
      "6. Sample Size Analysis:\n",
      "  Total observations: 934\n",
      "  Unique households: 462\n",
      "  Unique time periods: 3\n",
      "  Observations per group-time:\n",
      "     Treatment=0, Post=0: 334 obs\n",
      "     Treatment=0, Post=1: 0 obs\n",
      "     Treatment=1, Post=0: 291 obs\n",
      "     Treatment=1, Post=1: 309 obs\n",
      "============================================================\n",
      "\n",
      "\n",
      "  Outcome variable 'emp' summary:\n",
      "  Non-missing values: 934/934 (100.0%)\n",
      "  Mean: 0.578, Std: 0.494\n",
      "\n",
      "  DID Formula: emp ~ treat_post + treatment_group + vharvest + lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + EntityEffects + TimeEffects\n",
      "\n",
      "DID Results (PanelOLS with Entity and Time Fixed Effects):\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0175\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.3568\n",
      "No. Observations:                 857   R-squared (Within):               0.0267\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):             -0.2456\n",
      "Time:                        15:47:50   Log-likelihood                   -82.413\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      0.7929\n",
      "Entities:                         445   P-value                           0.6230\n",
      "Avg Obs:                       1.9258   Distribution:                   F(9,401)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             26.159\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                       3   Distribution:                   F(9,401)\n",
      "Avg Obs:                       285.67                                           \n",
      "Min Obs:                       109.00                                           \n",
      "Max Obs:                       443.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_post           0.0403     0.0852     0.4724     0.6369     -0.1273      0.2078\n",
      "treatment_group      0.6483     0.8684     0.7465     0.4558     -1.0589      2.3555\n",
      "vharvest         -3.814e-08  7.771e-08    -0.4908     0.6238  -1.909e-07   1.146e-07\n",
      "lognf                0.0082     0.0066     1.2417     0.2151     -0.0048      0.0213\n",
      "educh                0.0304     0.0829     0.3665     0.7142     -0.1326      0.1933\n",
      "head_age             0.0049     0.0101     0.4873     0.6263     -0.0149      0.0247\n",
      "head_sex            -0.1936     0.1132    -1.7101     0.0880     -0.4163      0.0290\n",
      "dependency_ratio    -0.0075     0.0481    -0.1563     0.8759     -0.1021      0.0870\n",
      "farmsize             0.0117     0.0243     0.4814     0.6305     -0.0361      0.0596\n",
      "asset                0.0226     0.0391     0.5768     0.5644     -0.0543      0.0995\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 1.7198\n",
      "P-value: 0.0000\n",
      "Distribution: F(446,401)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "--- Running Parallel Trends Test for Nigeria - cc ---\n",
      "Event times for treated units: [-6.0, -3.0, 0.0, 3.0]\n",
      "Omitted period (reference): -3.0\n",
      "\n",
      "Event study formula: emp ~ treat_event_m6.0 + treat_event_0.0 + treat_event_3.0 + EntityEffects + TimeEffects\n",
      "\n",
      "Event Study Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                    emp   R-squared:                        0.0084\n",
      "Estimator:                   PanelOLS   R-squared (Between):              0.0504\n",
      "No. Observations:                 934   R-squared (Within):               0.0197\n",
      "Date:                Thu, Aug 21 2025   R-squared (Overall):              0.0539\n",
      "Time:                        15:47:50   Log-likelihood                   -135.88\n",
      "Cov. Estimator:             Clustered                                           \n",
      "                                        F-statistic:                      1.3199\n",
      "Entities:                         462   P-value                           0.2672\n",
      "Avg Obs:                       2.0216   Distribution:                   F(3,467)\n",
      "Min Obs:                       1.0000                                           \n",
      "Max Obs:                       3.0000   F-statistic (robust):             0.6859\n",
      "                                        P-value                           0.5610\n",
      "Time periods:                       3   Distribution:                   F(3,467)\n",
      "Avg Obs:                       311.33                                           \n",
      "Min Obs:                       128.00                                           \n",
      "Max Obs:                       462.00                                           \n",
      "                                                                                \n",
      "                                Parameter Estimates                                 \n",
      "====================================================================================\n",
      "                  Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------------\n",
      "treat_event_m6.0    -0.1145     0.1353    -0.8464     0.3978     -0.3804      0.1514\n",
      "treat_event_0.0      0.0840     0.0788     1.0658     0.2870     -0.0709      0.2390\n",
      "treat_event_3.0      0.1775     0.1742     1.0188     0.3088     -0.1648      0.5197\n",
      "====================================================================================\n",
      "\n",
      "F-test for Poolability: 2.0996\n",
      "P-value: 0.0000\n",
      "Distribution: F(463,467)\n",
      "\n",
      "Included effects: Entity, Time\n",
      "\n",
      "✅ Event study plot saved as 'parallel_trends_Nigeria_cc.png'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\n",
      "   Chi2(1): 0.716  p-value: 0.397\n",
      "   ✅ PASS: No evidence of differential pre-trends\n",
      "\n",
      "📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\n",
      "   Chi2(2): 1.572  p-value: 0.456\n",
      "\n",
      "--- Running Placebo Test for Nigeria - cc ---\n",
      "Treatment cohorts: {2016.0: np.int64(464), 2019.0: np.int64(136)}\n",
      "\n",
      "Testing placebo treatment at t=2013 for cohort actually treated at t=2016.0\n",
      "  Skipping: Not enough pre-treatment periods\n",
      "\n",
      "Testing placebo treatment at t=2016 for cohort actually treated at t=2019.0\n",
      "  Coefficient: 0.2023 (SE: 0.1336)  p=0.1324  ✅ PASS\n",
      "\n",
      "--- Placebo Test Summary ---\n",
      "Passed: 1/1 tests\n",
      "\n",
      "Detailed Results:\n",
      "   cohort  placebo_year     coef        se      pval  n_treated  n_control  \\\n",
      "0  2019.0          2016  0.20227  0.133607  0.132421         54        199   \n",
      "\n",
      "         method  \n",
      "0  DID-pre-only  \n",
      "✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\n",
      "\n",
      "============================================================\n",
      "********** ALL ANALYSES COMPLETE **********\n",
      "============================================================\n",
      "\n",
      "\n",
      "Summary of Results:\n",
      "     Country   Analysis                           Description  \\\n",
      "0     Malawi        cci         Never sellers vs Late sellers   \n",
      "1     Malawi  cash_sale  CCI sellers: Never cash vs Late cash   \n",
      "2     Malawi         cc  CCI sellers: Never cert vs Late cert   \n",
      "3   Ethiopia        cci         Never sellers vs Late sellers   \n",
      "4   Ethiopia  cash_sale  CCI sellers: Never cash vs Late cash   \n",
      "5   Ethiopia         cc  CCI sellers: Never cert vs Late cert   \n",
      "6     Uganda        cci         Never sellers vs Late sellers   \n",
      "7     Uganda  cash_sale  CCI sellers: Never cash vs Late cash   \n",
      "8     Uganda         cc  CCI sellers: Never cert vs Late cert   \n",
      "9   Tanzania        cci         Never sellers vs Late sellers   \n",
      "10  Tanzania  cash_sale  CCI sellers: Never cash vs Late cash   \n",
      "11  Tanzania         cc  CCI sellers: Never cert vs Late cert   \n",
      "12   Nigeria        cci         Never sellers vs Late sellers   \n",
      "13   Nigeria  cash_sale  CCI sellers: Never cash vs Late cash   \n",
      "14   Nigeria         cc  CCI sellers: Never cert vs Late cert   \n",
      "\n",
      "       Matching Method  N Matched HH  Treatment Effect        SE  \\\n",
      "0   nearest_neighbor_1           507          0.263170  0.045909   \n",
      "1        mahalanobis_1           464          0.140068  0.047951   \n",
      "2   nearest_neighbor_1           173          0.066396  0.094565   \n",
      "3   nearest_neighbor_1           531          0.428869  0.076981   \n",
      "4   nearest_neighbor_1           550          0.157915  0.094498   \n",
      "5        mahalanobis_3           368          0.058116  0.129749   \n",
      "6        mahalanobis_1           564         -0.075372  0.046662   \n",
      "7   nearest_neighbor_1           682         -0.060252  0.034015   \n",
      "8   nearest_neighbor_3           423          0.001006  0.070501   \n",
      "9   nearest_neighbor_1           781          0.268454  0.055437   \n",
      "10       mahalanobis_1           728          0.059868  0.053680   \n",
      "11       mahalanobis_1           329         -0.080058  0.099628   \n",
      "12  nearest_neighbor_1          1085          0.120461  0.057721   \n",
      "13       mahalanobis_1           932          0.077936  0.049322   \n",
      "14       mahalanobis_1           462          0.040264  0.085228   \n",
      "\n",
      "         P-value  Mean Balance (SMD)  \n",
      "0   1.236665e-08            0.067733  \n",
      "1   3.550950e-03            0.077543  \n",
      "2   4.831925e-01            0.084937  \n",
      "3   4.384056e-08            0.066298  \n",
      "4   9.532405e-02            0.068110  \n",
      "5   6.546289e-01            0.055729  \n",
      "6   1.065393e-01            0.071379  \n",
      "7   7.670736e-02            0.066052  \n",
      "8   9.886237e-01            0.068940  \n",
      "9   1.495067e-06            0.041891  \n",
      "10  2.650012e-01            0.043229  \n",
      "11  4.222683e-01            0.049612  \n",
      "12  3.716243e-02            0.038584  \n",
      "13  1.143828e-01            0.030796  \n",
      "14  6.368754e-01            0.043034  \n",
      "\n",
      "Results saved to 'psm_did_results_summary_enhanced.csv'\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import statsmodels.api as sm\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from pandas.api.types import is_numeric_dtype\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "try:\n",
    "    from psmpy import PsmPy  # noqa: F401\n",
    "except Exception:\n",
    "    print(\"Installing psmpy...\")\n",
    "    import subprocess\n",
    "    try:\n",
    "        subprocess.check_call(['pip', 'install', 'psmpy'])\n",
    "        from psmpy import PsmPy  # noqa: F401\n",
    "    except Exception:\n",
    "        print(\"  Could not install psmpy; continuing without it.\")\n",
    "\n",
    "# For DID estimation\n",
    "try:\n",
    "    from linearmodels.panel import PanelOLS\n",
    "except Exception:\n",
    "    print(\"Installing linearmodels...\")\n",
    "    import subprocess\n",
    "    subprocess.check_call(['pip', 'install', 'linearmodels'])\n",
    "    from linearmodels.panel import PanelOLS\n",
    "\n",
    "\n",
    "class PSMDIDAnalysis:\n",
    "\n",
    "    def __init__(self, data_paths):\n",
    "        self.data_paths = data_paths\n",
    "        self.countries = list(data_paths.keys())\n",
    "\n",
    "        # Covariate sets\n",
    "        self.covariates = ['lognf', 'educh', 'head_age', 'head_sex',\n",
    "                           'dependency_ratio', 'farmsize', 'asset',\n",
    "                           'fadult', 'madult', 'educated', 'infra_index', 'vharvest'] # For matching in first experiment with entry to markets\n",
    "\n",
    "        self.covariates2 = ['cci', 'lognf', 'educh', 'head_age', 'head_sex',\n",
    "                            'dependency_ratio', 'farmsize', 'asset',\n",
    "                            'fadult', 'madult', 'educated', 'infra_index', 'vharvest'] # matching among sellers for second experiment\n",
    "\n",
    "        self.covariates3 = ['lognf', 'educh', 'head_age', 'head_sex',\n",
    "                            'dependency_ratio', 'vharvest', 'farmsize', 'asset'] # reg variables\n",
    "\n",
    "        self.covariates4 = ['vharvest', 'lognf', 'educh',\n",
    "                            'head_age', 'head_sex', 'dependency_ratio',\n",
    "                            'farmsize', 'asset'] # reg variables\n",
    "\n",
    "        # Matching methods to try\n",
    "        self.matching_methods = {\n",
    "            'nearest_neighbor_1': {'method': 'nearest', 'n_neighbors': 1, 'caliper': 0.5},\n",
    "            'nearest_neighbor_3': {'method': 'nearest', 'n_neighbors': 3, 'caliper': 0.5},\n",
    "            'nearest_neighbor_5': {'method': 'nearest', 'n_neighbors': 5, 'caliper': 0.5},\n",
    "            'mahalanobis_1': {'method': 'mahalanobis', 'n_neighbors': 1},\n",
    "            'mahalanobis_3': {'method': 'mahalanobis', 'n_neighbors': 3},\n",
    "            'kernel': {'method': 'kernel', 'bandwidth': 0.1},\n",
    "            'radius': {'method': 'radius', 'radius': 0.1},\n",
    "            'stratification': {'method': 'stratification', 'n_strata': 5}\n",
    "        }\n",
    "\n",
    "    # ----------------------------\n",
    "    # Data Loading / Preparation\n",
    "    # ----------------------------\n",
    "    def load_data(self, country):\n",
    "        try:\n",
    "            path = self.data_paths[country]\n",
    "            if path.endswith('.dta'):\n",
    "                df = pd.read_stata(path)\n",
    "            elif path.endswith('.csv'):\n",
    "                df = pd.read_csv(path)\n",
    "            else:\n",
    "                raise ValueError(f\"Unsupported file format for {country}: {path}\")\n",
    "\n",
    "            # Ensure key variables are numeric where relevant\n",
    "            numeric_vars = ['id', 't', 'cci', 'cash_sale', 'emp', 'Netnfcashinc', 'cc']\n",
    "            for var in numeric_vars:\n",
    "                if var in df.columns:\n",
    "                    df[var] = pd.to_numeric(df[var], errors='coerce')\n",
    "\n",
    "            # Handle cash_sale NAs as 0\n",
    "            if 'cash_sale' in df.columns:\n",
    "                df['cash_sale'] = df['cash_sale'].fillna(0)\n",
    "\n",
    "            # Create log of non-farm income\n",
    "            if 'Netnfcashinc' in df.columns:\n",
    "                df['lognf'] = np.log(df['Netnfcashinc'].fillna(0) + 1)\n",
    "            else:\n",
    "                print(f\"Warning: 'Netnfcashinc' not found in {country} data; setting 'lognf' to 0.\")\n",
    "                df['lognf'] = 0\n",
    "\n",
    "            # Required variables\n",
    "            required_vars = ['id', 't']\n",
    "            missing = [var for var in required_vars if var not in df.columns]\n",
    "            if missing:\n",
    "                print(f\"Error: Missing required variables in {country}: {missing}\")\n",
    "                return None\n",
    "\n",
    "            df = df.dropna(subset=['id', 't'])\n",
    "            print(f\"\\n{country} data loaded:\")\n",
    "            print(f\"  Observations: {len(df)}\")\n",
    "            print(f\"  Time periods: {sorted(pd.unique(df['t']))}\")\n",
    "            print(f\"  Number of households: {df['id'].nunique()}\")\n",
    "\n",
    "            if 'cc' in df.columns:\n",
    "                print(f\"  'cc' variable found with {df['cc'].notna().sum()} non-missing values\")\n",
    "\n",
    "            return df\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error loading data for {country}: {e}\")\n",
    "            return None\n",
    "\n",
    "    def define_treatment_group(self, df, treatment_var='cci', country='', analysis_start_year=None,\n",
    "                               sellers_only=False, restrict_to_cci_sellers=False):\n",
    "        \"\"\"\n",
    "        Define treatment and control groups based on timing of first 'treatment_var' > 0.\n",
    "        \"\"\"\n",
    "        # Restrict to CCI sellers if requested\n",
    "        if restrict_to_cci_sellers and 'cci' in df.columns:\n",
    "            cci_sellers = df.groupby('id')['cci'].apply(lambda x: (x > 0).any())\n",
    "            cci_seller_ids = cci_sellers[cci_sellers].index\n",
    "            df = df[df['id'].isin(cci_seller_ids)].copy()\n",
    "            print(f\"  Restricted to CCI sellers: {len(cci_seller_ids)} households\")\n",
    "\n",
    "        df = df.dropna(subset=['id'])\n",
    "        df[treatment_var] = df[treatment_var].fillna(0)\n",
    "        df = df.sort_values(['id', 't'])\n",
    "\n",
    "        if df.empty or 't' not in df.columns or df['t'].nunique() == 0:\n",
    "            return pd.DataFrame()\n",
    "\n",
    "        all_years = sorted(df['t'].unique())\n",
    "        if analysis_start_year is None:\n",
    "            analysis_start_year = all_years[0]\n",
    "\n",
    "        # Households that have baseline dat\n",
    "        households_at_baseline = df[df['t'] == analysis_start_year]['id'].unique()\n",
    "        df = df[df['id'].isin(households_at_baseline)].copy()\n",
    "\n",
    "        print(f\"\\n  Filtering to households present at baseline year {analysis_start_year}\")\n",
    "        print(f\"  Households with baseline data: {len(households_at_baseline)}\")\n",
    "\n",
    "        if restrict_to_cci_sellers:\n",
    "            print(f\"  Note: This analysis is restricted to CCI sellers\")\n",
    "\n",
    "        # Household-level indicators\n",
    "        household_df = df.groupby('id').agg({\n",
    "            treatment_var: ['max', lambda x: (x > 0).any()],\n",
    "            't': 'min'\n",
    "        }).reset_index()\n",
    "        household_df.columns = ['id', 'max_sales', 'ever_sold', 'first_year']\n",
    "\n",
    "        baseline_sales = df[df['t'] == analysis_start_year].groupby('id')[treatment_var].max() > 0\n",
    "        household_df['sold_at_baseline'] = household_df['id'].map(baseline_sales).fillna(False)\n",
    "\n",
    "        first_sale_df = df[df[treatment_var] > 0].groupby('id')['t'].min().reset_index()\n",
    "        first_sale_df.columns = ['id', 'first_sale']\n",
    "        household_df = household_df.merge(first_sale_df, on='id', how='left')\n",
    "\n",
    "        # Assign treatment-group\n",
    "        household_df['treatment_group'] = np.nan\n",
    "\n",
    "        if restrict_to_cci_sellers and treatment_var == 'cash_sale':\n",
    "            # Control: CCI sellers who never sold cash crops\n",
    "            household_df.loc[household_df['ever_sold'] == False, 'treatment_group'] = 0\n",
    "            # Treatment: start selling cash crops after baseline\n",
    "            household_df.loc[\n",
    "                (household_df['sold_at_baseline'] == False) &\n",
    "                (household_df['ever_sold'] == True) &\n",
    "                (household_df['first_sale'] > analysis_start_year),\n",
    "                'treatment_group'\n",
    "            ] = 1\n",
    "            print(f\"\\n  Treatment assignment for {treatment_var} (among CCI sellers):\")\n",
    "            print(f\"  Control: CCI sellers who never sell cash crops\")\n",
    "            print(f\"  Treatment: CCI sellers who start selling cash crops after baseline\")\n",
    "\n",
    "        elif sellers_only:\n",
    "            sellers_df = household_df[household_df['ever_sold'] & (~household_df['sold_at_baseline'])].copy()\n",
    "\n",
    "            if len(sellers_df) > 0:\n",
    "                median_sale_year = sellers_df['first_sale'].median()\n",
    "                print(f\"  Median first {treatment_var} year: {median_sale_year}\")\n",
    "\n",
    "                household_df.loc[\n",
    "                    (household_df['ever_sold'] == True) &\n",
    "                    (household_df['sold_at_baseline'] == False) &\n",
    "                    (household_df['first_sale'] <= median_sale_year),\n",
    "                    'treatment_group'\n",
    "                ] = 0\n",
    "\n",
    "                household_df.loc[\n",
    "                    (household_df['ever_sold'] == True) &\n",
    "                    (household_df['first_sale'] > median_sale_year),\n",
    "                    'treatment_group'\n",
    "                ] = 1\n",
    "\n",
    "            print(f\"\\n  Treatment assignment for {treatment_var} (early vs late adopters):\")\n",
    "            print(f\"  Analysis restricted to households that adopt after baseline\")\n",
    "            print(f\"  Control: Adopted on/ before median year ({median_sale_year})\")\n",
    "            print(f\"  Treatment: Adopted after median year ({median_sale_year})\")\n",
    "\n",
    "        else:\n",
    "            # Standard: Control = never adopt; Treatment = adopt after baseline\n",
    "            household_df.loc[household_df['ever_sold'] == False, 'treatment_group'] = 0\n",
    "            household_df.loc[\n",
    "                (household_df['sold_at_baseline'] == False) &\n",
    "                (household_df['ever_sold'] == True) &\n",
    "                (household_df['first_sale'] > analysis_start_year),\n",
    "                'treatment_group'\n",
    "            ] = 1\n",
    "\n",
    "            print(f\"\\n  Treatment assignment for {treatment_var}:\")\n",
    "            print(f\"  Standard analysis (never-adopters vs late-adopters)\")\n",
    "\n",
    "        df = df.merge(household_df[['id', 'treatment_group', 'first_sale']], on='id', how='left')\n",
    "        final_df = df[df['treatment_group'].isin([0, 1])].copy()\n",
    "\n",
    "        # Diagnostics\n",
    "        n_control = household_df[household_df['treatment_group'] == 0]['id'].nunique()\n",
    "        n_treatment = household_df[household_df['treatment_group'] == 1]['id'].nunique()\n",
    "        n_excluded = len(households_at_baseline) - n_control - n_treatment\n",
    "\n",
    "        print(f\"  Baseline year: {analysis_start_year}\")\n",
    "        print(f\"  Total households in data: {household_df['id'].nunique()}\")\n",
    "        print(f\"  Control group: {n_control} households\")\n",
    "        print(f\"  Treatment group: {n_treatment} households\")\n",
    "        print(f\"  Excluded: {n_excluded} households\")\n",
    "\n",
    "        return final_df\n",
    "\n",
    "    # Matching Utilities\n",
    "    # ----------------------------\n",
    "    def calculate_propensity_scores(self, X, treatment):\n",
    "        \"\"\"Calculate propensity scores using logistic regression\"\"\"\n",
    "        try:\n",
    "            scaler = StandardScaler()\n",
    "            X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "            lr = LogisticRegression(random_state=42, max_iter=1000)\n",
    "            lr.fit(X_scaled, treatment)\n",
    "            ps = lr.predict_proba(X_scaled)[:, 1]\n",
    "            return ps\n",
    "        except Exception as e:\n",
    "            print(f\"  Error in propensity score calculation: {e}\")\n",
    "            print(f\"  X shape: {X.shape}, X dtypes: {X.dtypes.value_counts()}\")\n",
    "            raise\n",
    "\n",
    "    def match_nearest_neighbor(self, df_base, ps, treatment, n_neighbors=1, caliper=0.5):\n",
    "        \"\"\"Nearest neighbor matching with caliper\"\"\"\n",
    "        treated_idx = np.where(treatment == 1)[0]\n",
    "        control_idx = np.where(treatment == 0)[0]\n",
    "\n",
    "        ps_treated = ps[treated_idx]\n",
    "        ps_control = ps[control_idx]\n",
    "\n",
    "        if len(control_idx) == 0 or len(treated_idx) == 0:\n",
    "            return np.zeros(len(df_base), dtype=bool)\n",
    "\n",
    "        nn = NearestNeighbors(n_neighbors=n_neighbors)\n",
    "        nn.fit(ps_control.reshape(-1, 1))\n",
    "\n",
    "        matched_controls = []\n",
    "        matched_treated = []\n",
    "\n",
    "        for i, ps_t in enumerate(ps_treated):\n",
    "            distances, indices = nn.kneighbors(ps_t.reshape(-1, 1))\n",
    "            valid_matches = indices[0][distances[0] <= caliper]\n",
    "            if len(valid_matches) > 0:\n",
    "                matched_treated.append(treated_idx[i])\n",
    "                for idx in valid_matches:\n",
    "                    matched_controls.append(control_idx[idx])\n",
    "\n",
    "        matched = np.zeros(len(df_base), dtype=int)\n",
    "        if matched_treated:\n",
    "            matched[matched_treated] = 1\n",
    "        if matched_controls:\n",
    "            matched[list(set(matched_controls))] = 1\n",
    "\n",
    "        return matched.astype(bool)\n",
    "\n",
    "    def match_mahalanobis(self, X, treatment, n_neighbors=1):\n",
    "        scaler = StandardScaler()\n",
    "        X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "        treated_idx = np.where(treatment == 1)[0]\n",
    "        control_idx = np.where(treatment == 0)[0]\n",
    "\n",
    "        if len(control_idx) == 0 or len(treated_idx) == 0:\n",
    "            return np.zeros(len(X), dtype=bool)\n",
    "\n",
    "        X_treated_scaled = X_scaled[treated_idx]\n",
    "        X_control_scaled = X_scaled[control_idx]\n",
    "\n",
    "        cov_matrix = np.cov(X_control_scaled, rowvar=False)\n",
    "        inv_cov_matrix = np.linalg.pinv(cov_matrix)\n",
    "\n",
    "        nn = NearestNeighbors(n_neighbors=n_neighbors, metric='mahalanobis',\n",
    "                              metric_params={'VI': inv_cov_matrix})\n",
    "        nn.fit(X_control_scaled)\n",
    "\n",
    "        distances, indices = nn.kneighbors(X_treated_scaled)\n",
    "        matched_control_indices = set(control_idx[indices.flatten()])\n",
    "        matched_treated_indices = list(treated_idx)\n",
    "\n",
    "        matched_mask = np.zeros(len(X), dtype=bool)\n",
    "        if matched_control_indices:\n",
    "            matched_mask[list(matched_control_indices)] = True\n",
    "        if matched_treated_indices:\n",
    "            matched_mask[matched_treated_indices] = True\n",
    "\n",
    "        return matched_mask\n",
    "\n",
    "    def match_kernel(self, ps, treatment, bandwidth=0.1):\n",
    "        matched = np.ones(len(treatment), dtype=bool)\n",
    "        return matched\n",
    "\n",
    "    def match_radius(self, ps, treatment, radius=0.1):\n",
    "        treated_idx = np.where(treatment == 1)[0]\n",
    "        control_idx = np.where(treatment == 0)[0]\n",
    "        if len(treated_idx) == 0 or len(control_idx) == 0:\n",
    "            return np.zeros(len(treatment), dtype=bool)\n",
    "\n",
    "        ps_treated = ps[treated_idx]\n",
    "        ps_control = ps[control_idx]\n",
    "\n",
    "        matched_controls = set()\n",
    "        matched_treated = []\n",
    "\n",
    "        for i, ps_t in enumerate(ps_treated):\n",
    "            within_radius = control_idx[np.abs(ps_control - ps_t) <= radius]\n",
    "            if len(within_radius) > 0:\n",
    "                matched_treated.append(treated_idx[i])\n",
    "                matched_controls.update(within_radius)\n",
    "\n",
    "        matched = np.zeros(len(treatment))\n",
    "        if matched_treated:\n",
    "            matched[matched_treated] = 1\n",
    "        if matched_controls:\n",
    "            matched[list(matched_controls)] = 1\n",
    "\n",
    "        return matched.astype(bool)\n",
    "\n",
    "    def match_stratification(self, ps, treatment, n_strata=5):\n",
    "        try:\n",
    "            strata = pd.qcut(ps, q=n_strata, labels=False, duplicates='drop')\n",
    "        except ValueError:\n",
    "            print(\"  Warning: Could not create desired number of strata, reducing strata.\")\n",
    "            strata = pd.qcut(ps, q=max(1, n_strata - 1), labels=False, duplicates='drop')\n",
    "\n",
    "        matched = np.zeros(len(treatment), dtype=bool)\n",
    "        for s in range(int(strata.max()) + 1):\n",
    "            in_stratum = strata == s\n",
    "            n_treated = np.sum(treatment[in_stratum] == 1)\n",
    "            n_control = np.sum(treatment[in_stratum] == 0)\n",
    "            if n_treated > 0 and n_control > 0:\n",
    "                matched[in_stratum] = True\n",
    "\n",
    "        return matched\n",
    "\n",
    "    def check_balance(self, X, treatment, matched):\n",
    "        if np.sum(matched) == 0:\n",
    "            return pd.DataFrame(), np.nan, np.nan\n",
    "\n",
    "        X_matched = X[matched]\n",
    "        treatment_matched = treatment[matched]\n",
    "\n",
    "        if len(np.unique(treatment_matched)) < 2:\n",
    "            print(\"  Warning: Matched sample contains only one group.\")\n",
    "            return pd.DataFrame(), np.nan, np.nan\n",
    "\n",
    "        balance_stats = []\n",
    "        for col in X.columns:\n",
    "            mean_treated = X_matched.loc[treatment_matched == 1, col].mean()\n",
    "            mean_control = X_matched.loc[treatment_matched == 0, col].mean()\n",
    "            std_original_treated = X.loc[treatment == 1, col].std()\n",
    "            smd = 0 if std_original_treated == 0 else abs(mean_treated - mean_control) / std_original_treated\n",
    "            balance_stats.append({\n",
    "                'variable': col,\n",
    "                'smd': smd,\n",
    "                'mean_treated': mean_treated,\n",
    "                'mean_control': mean_control\n",
    "            })\n",
    "\n",
    "        balance_df = pd.DataFrame(balance_stats)\n",
    "        mean_smd = balance_df['smd'].mean() if not balance_df.empty else np.nan\n",
    "        max_smd = balance_df['smd'].max() if not balance_df.empty else np.nan\n",
    "        return balance_df, mean_smd, max_smd\n",
    "\n",
    "    def select_best_matching_method(self, df_base, treatment_var, covariates,\n",
    "                                    min_retention=0.5, max_smd_thresh=0.1):\n",
    "\n",
    "        results = {}\n",
    "\n",
    "        available_covariates = [c for c in covariates if c in df_base.columns]\n",
    "        missing_covariates = [c for c in covariates if c not in df_base.columns]\n",
    "\n",
    "        if missing_covariates:\n",
    "            print(f\"\\nWarning: Missing covariates: {missing_covariates}\")\n",
    "            print(f\"Using available covariates: {available_covariates}\")\n",
    "\n",
    "        if not available_covariates:\n",
    "            print(\"Error: No covariates available for matching\")\n",
    "            return None, None\n",
    "\n",
    "        X = df_base[available_covariates].copy()\n",
    "\n",
    "        for col in X.columns:\n",
    "            if X[col].isna().any():\n",
    "                if is_numeric_dtype(X[col]):\n",
    "                    X[col] = X[col].fillna(X[col].median())\n",
    "                else:\n",
    "                    X[col] = X[col].fillna(X[col].mode()[0] if not X[col].mode().empty else 'missing')\n",
    "\n",
    "        for col in list(X.columns):\n",
    "            if X[col].dtype == 'object' or isinstance(X[col].dtype, pd.CategoricalDtype):\n",
    "                print(f\"  Converting categorical variable '{col}' to numeric\")\n",
    "                if X[col].isna().any():\n",
    "                    X[col] = X[col].fillna('missing')\n",
    "\n",
    "                unique_vals = X[col].dropna().unique()\n",
    "                if len(unique_vals) <= 2:\n",
    "                    mapping = {unique_vals[0]: 0}\n",
    "                    if len(unique_vals) == 2:\n",
    "                        mapping[unique_vals[1]] = 1\n",
    "                    if 'missing' in X[col].values and 'missing' not in mapping:\n",
    "                        mapping['missing'] = 0\n",
    "                    X[col] = X[col].map(mapping).astype(float)\n",
    "                else:\n",
    "                    dummies = pd.get_dummies(X[col], prefix=col, drop_first=True)\n",
    "                    X = pd.concat([X.drop(columns=[col]), dummies], axis=1)\n",
    "\n",
    "        non_numeric = [col for col in X.columns if not is_numeric_dtype(X[col])]\n",
    "        if non_numeric:\n",
    "            print(f\"  Warning: Non-numeric columns remain after conversion: {non_numeric}\")\n",
    "            print(f\"  Dropping non-numeric columns: {non_numeric}\")\n",
    "            X = X.select_dtypes(include=[np.number])\n",
    "\n",
    "        treatment = df_base['treatment_group'].values\n",
    "        unique_treatment = np.unique(treatment)\n",
    "        print(f\"\\n  Unique treatment values found: {unique_treatment}\")\n",
    "        print(f\"  Control households: {np.sum(treatment == 0)}\")\n",
    "        print(f\"  Treatment households: {np.sum(treatment == 1)}\")\n",
    "\n",
    "        if len(unique_treatment) < 2:\n",
    "            print(\"Error: Only one treatment group found in baseline data. Cannot perform matching.\")\n",
    "            return None, None\n",
    "\n",
    "        try:\n",
    "            ps = self.calculate_propensity_scores(X, treatment)\n",
    "        except Exception as e:\n",
    "            print(f\"Error calculating propensity scores: {e}\")\n",
    "            return None, None\n",
    "\n",
    "        print(\"\\nTrying different matching methods...\")\n",
    "\n",
    "        for method_name, params in self.matching_methods.items():\n",
    "            print(f\"\\n{method_name}:\")\n",
    "            try:\n",
    "                if params['method'] == 'nearest':\n",
    "                    matched = self.match_nearest_neighbor(df_base, ps, treatment,\n",
    "                                                          n_neighbors=params['n_neighbors'],\n",
    "                                                          caliper=params['caliper'])\n",
    "                elif params['method'] == 'kernel':\n",
    "                    matched = self.match_kernel(ps, treatment, bandwidth=params['bandwidth'])\n",
    "                elif params['method'] == 'mahalanobis':\n",
    "                    print(\"  Performing Mahalanobis Distance Matching...\")\n",
    "                    matched = self.match_mahalanobis(X, treatment, n_neighbors=params['n_neighbors'])\n",
    "                elif params['method'] == 'radius':\n",
    "                    matched = self.match_radius(ps, treatment, radius=params['radius'])\n",
    "                elif params['method'] == 'stratification':\n",
    "                    matched = self.match_stratification(ps, treatment, n_strata=params['n_strata'])\n",
    "                else:\n",
    "                    continue\n",
    "\n",
    "                n_matched = int(np.sum(matched))\n",
    "                retention_rate = n_matched / len(df_base) if len(df_base) > 0 else 0.0\n",
    "\n",
    "                balance_df, mean_smd, max_smd_val = self.check_balance(X, treatment, matched)\n",
    "\n",
    "                print(f\"  Matched: {n_matched}/{len(df_base)} ({retention_rate:.1%})\")\n",
    "                print(f\"  Mean SMD: {mean_smd:.3f}\")\n",
    "                print(f\"  Max SMD: {max_smd_val:.3f}\")\n",
    "\n",
    "                results[method_name] = {\n",
    "                    'matched': matched,\n",
    "                    'n_matched': n_matched,\n",
    "                    'retention_rate': retention_rate,\n",
    "                    'mean_smd': mean_smd,\n",
    "                    'max_smd': max_smd_val,\n",
    "                    'balance_df': balance_df\n",
    "                }\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"  Error in {method_name}: {e}\")\n",
    "                continue\n",
    "\n",
    "        if not results:\n",
    "            print(\"\\nError: No matching method succeeded\")\n",
    "            return None, None\n",
    "\n",
    "        valid_methods = {k: v for k, v in results.items()\n",
    "                         if v['retention_rate'] >= min_retention and\n",
    "                         (v['mean_smd'] if v['mean_smd'] is not None else np.inf) <= max_smd_thresh and\n",
    "                         v['n_matched'] > 0}\n",
    "\n",
    "        if not valid_methods:\n",
    "            print(\"\\nNo method meets the strict criteria. Relaxing balance requirement...\")\n",
    "            valid_methods = {k: v for k, v in results.items()\n",
    "                             if v['retention_rate'] >= min_retention and v['n_matched'] > 0}\n",
    "\n",
    "        if valid_methods:\n",
    "            best_method_name = min(valid_methods.keys(), key=lambda k: valid_methods[k]['mean_smd'])\n",
    "            print(f\"\\nSelected method: {best_method_name}\")\n",
    "            return best_method_name, results[best_method_name]\n",
    "        else:\n",
    "            print(\"\\nNo valid matching method found. Using method with highest retention (with matches).\")\n",
    "            results_with_matches = {k: v for k, v in results.items() if v['n_matched'] > 0}\n",
    "            if not results_with_matches:\n",
    "                print(\"All matching methods failed to find any matches.\")\n",
    "                return None, None\n",
    "            best_method_name = max(results_with_matches.keys(),\n",
    "                                   key=lambda k: results_with_matches[k]['retention_rate'])\n",
    "            return best_method_name, results_with_matches[best_method_name]\n",
    "\n",
    "\n",
    "    # DID Estimation\n",
    "\n",
    "    def estimate_did(self, df, outcome='emp', covariates=None, country='', analysis_type=''):\n",
    "        df = df.copy()\n",
    "        df['treatment_group'] = pd.to_numeric(df['treatment_group'], errors='coerce')\n",
    "\n",
    "        # post indicator\n",
    "        df['post'] = (df['t'] >= df['first_sale']).astype(int)\n",
    "        df.loc[df['treatment_group'] == 0, 'post'] = 0\n",
    "        df['treat_post'] = df['treatment_group'] * df['post']\n",
    "\n",
    "        analysis_desc = {\n",
    "            'cci': 'CCI (never sellers vs late sellers)',\n",
    "            'cash_sale': 'Cash Sale (CCI sellers: never cash vs late cash)',\n",
    "            'cc': 'CC (CCI sellers: never certified vs late certified)'\n",
    "        }\n",
    "\n",
    "        print(\"\\n\" + \"=\" * 60)\n",
    "        print(f\"ENHANCED DEBUGGING - {country} - {analysis_desc.get(analysis_type, analysis_type)}\")\n",
    "        print(\"=\" * 60)\n",
    "\n",
    "        # 1. Group-Time Combinations\n",
    "        print(\"\\n1. Group-Time Combinations:\")\n",
    "        crosstab = pd.crosstab(df['treatment_group'], df['post'], margins=True)\n",
    "        print(crosstab)\n",
    "\n",
    "        issues_found = False\n",
    "        for treat in [0, 1]:\n",
    "            for post in [0, 1]:\n",
    "                try:\n",
    "                    count = int(crosstab.loc[treat, post])\n",
    "                    if count == 0:\n",
    "                        print(f\"    WARNING: No observations for treatment={treat}, post={post}\")\n",
    "                        issues_found = True\n",
    "                except Exception:\n",
    "                    print(f\"    WARNING: No observations for treatment={treat}, post={post}\")\n",
    "                    issues_found = True\n",
    "\n",
    "        if not issues_found:\n",
    "            print(\"  ✓ All group-time combinations have observations\")\n",
    "\n",
    "        # 2. Panel structure\n",
    "        print(\"\\n2. Panel Structure:\")\n",
    "        panel_check = df.groupby(['id', 't']).size()\n",
    "        duplicates = panel_check[panel_check > 1]\n",
    "        if len(duplicates) > 0:\n",
    "            print(f\"    WARNING: Found {len(duplicates)} duplicate id-time combinations\")\n",
    "            print(f\"     First 5 duplicates: {duplicates.head()}\")\n",
    "\n",
    "        # 3. Time periods by treatment group\n",
    "        print(\"\\n3. Time Periods by Treatment Group:\")\n",
    "        for treat in [0, 1]:\n",
    "            time_periods = sorted(df[df['treatment_group'] == treat]['t'].unique())\n",
    "            print(f\"  Treatment={treat}: {time_periods}\")\n",
    "\n",
    "        # 4. Variance checks\n",
    "        print(\"\\n4. Variable Variance Check:\")\n",
    "        key_vars = ['treat_post', 'treatment_group', 'post', outcome]\n",
    "        if covariates:\n",
    "            key_vars.extend([c for c in covariates if c in df.columns])\n",
    "\n",
    "        for var in key_vars:\n",
    "            if var in df.columns:\n",
    "                unique_vals = df[var].nunique()\n",
    "                if pd.api.types.is_numeric_dtype(df[var]):\n",
    "                    variance = df[var].var()\n",
    "                    print(f\"  {var}: variance={variance:.6f}, unique_values={unique_vals}\")\n",
    "                    if variance == 0:\n",
    "                        print(f\"       ZERO VARIANCE DETECTED!\")\n",
    "                else:\n",
    "                    print(f\"  {var}: categorical variable, unique_values={unique_vals}\")\n",
    "                    if unique_vals == 1:\n",
    "                        print(f\"       ONLY ONE UNIQUE VALUE!\")\n",
    "\n",
    "        # 5. Multicollinearity (high correlations)\n",
    "        print(\"\\n5. Checking for Perfect Multicollinearity:\")\n",
    "        numeric_cols = df.select_dtypes(include=[np.number]).columns\n",
    "        relevant_cols = [c for c in numeric_cols if c in key_vars]\n",
    "        valid_cols = [col for col in relevant_cols if df[col].var() > 0]\n",
    "        if len(valid_cols) > 1:\n",
    "            corr_matrix = df[valid_cols].corr()\n",
    "            high_corr = np.where(np.abs(corr_matrix) > 0.95)\n",
    "            for i, j in zip(high_corr[0], high_corr[1]):\n",
    "                if i < j:\n",
    "                    var1, var2 = valid_cols[i], valid_cols[j]\n",
    "                    corr_val = corr_matrix.iloc[i, j]\n",
    "                    if abs(corr_val) > 0.95 and var1 != var2:\n",
    "                        print(f\"    High correlation ({corr_val:.3f}) between {var1} and {var2}\")\n",
    "\n",
    "        # 6. Sample size\n",
    "        print(\"\\n6. Sample Size Analysis:\")\n",
    "        print(f\"  Total observations: {len(df)}\")\n",
    "        print(f\"  Unique households: {df['id'].nunique()}\")\n",
    "        print(f\"  Unique time periods: {df['t'].nunique()}\")\n",
    "        print(f\"  Observations per group-time:\")\n",
    "        for treat in [0, 1]:\n",
    "            for post_val in [0, 1]:\n",
    "                count = len(df[(df['treatment_group'] == treat) & (df['post'] == post_val)])\n",
    "                print(f\"     Treatment={treat}, Post={post_val}: {count} obs\")\n",
    "        print(\"=\" * 60 + \"\\n\")\n",
    "\n",
    "        # Outcome checks\n",
    "        if outcome not in df.columns:\n",
    "            print(f\"Error: Outcome variable '{outcome}' not found in data.\")\n",
    "            return None\n",
    "        if df[outcome].isna().all():\n",
    "            print(f\"Error: Outcome variable '{outcome}' is all missing.\")\n",
    "            return None\n",
    "\n",
    "        print(f\"\\n  Outcome variable '{outcome}' summary:\")\n",
    "        print(f\"  Non-missing values: {df[outcome].notna().sum()}/{len(df)} \"\n",
    "              f\"({df[outcome].notna().mean():.1%})\")\n",
    "        print(f\"  Mean: {df[outcome].mean():.3f}, Std: {df[outcome].std():.3f}\")\n",
    "\n",
    "        # DID formula (Entity & Time FE)\n",
    "        formula = f\"{outcome} ~ treat_post + treatment_group + EntityEffects + TimeEffects\"\n",
    "        if covariates:\n",
    "            available_covs = [c for c in covariates if c in df.columns and df[c].notna().any()]\n",
    "            if available_covs:\n",
    "                cov_formula = ' + '.join(available_covs)\n",
    "                formula = f\"{outcome} ~ treat_post + treatment_group + {cov_formula} + EntityEffects + TimeEffects\"\n",
    "\n",
    "        print(f\"\\n  DID Formula: {formula}\")\n",
    "\n",
    "        try:\n",
    "            df_reg = df.copy().reset_index(drop=True)\n",
    "            df_panel = df_reg.set_index(['id', 't'])\n",
    "            model = PanelOLS.from_formula(formula, data=df_panel, drop_absorbed=True, check_rank=False)\n",
    "            fit = model.fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "            print(\"\\nDID Results (PanelOLS with Entity and Time Fixed Effects):\")\n",
    "            print(fit)\n",
    "            return fit\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"Error in PanelOLS DID estimation: {e}\")\n",
    "            try:\n",
    "                print(\"\\nTrying alternative approach with manual time dummies...\")\n",
    "                df_reg = df.copy().reset_index(drop=True)\n",
    "                time_dummies = pd.get_dummies(df_reg['t'], prefix='t', drop_first=True)\n",
    "                df_reg = pd.concat([df_reg, time_dummies], axis=1)\n",
    "\n",
    "                time_vars = ' + '.join(time_dummies.columns)\n",
    "                formula_alt = f\"{outcome} ~ treat_post + treatment_group + {time_vars} + EntityEffects\"\n",
    "                if covariates:\n",
    "                    available_covs = [c for c in covariates if c in df_reg.columns and df_reg[c].notna().any()]\n",
    "                    if available_covs:\n",
    "                        cov_formula = ' + '.join(available_covs)\n",
    "                        formula_alt = f\"{outcome} ~ treat_post + treatment_group + {time_vars} + {cov_formula} + EntityEffects\"\n",
    "\n",
    "                df_panel = df_reg.set_index(['id', 't'])\n",
    "                model = PanelOLS.from_formula(formula_alt, data=df_panel, drop_absorbed=True, check_rank=False)\n",
    "                fit = model.fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "                print(\"\\nDID Results (with manual time dummies):\")\n",
    "                print(fit)\n",
    "                return fit\n",
    "\n",
    "            except Exception as e2:\n",
    "                print(f\"Alternative approach also failed: {e2}\")\n",
    "                return None\n",
    "\n",
    "\n",
    "    # Event Study & Placebo Tests\n",
    "\n",
    "    def _wald_joint_test(self, results, param_names):\n",
    "\n",
    "        names = [p for p in param_names if p in results.params.index]\n",
    "        if len(names) == 0:\n",
    "            return None\n",
    "\n",
    "        b = results.params[names].values\n",
    "        V = results.cov.loc[names, names].values\n",
    "        try:\n",
    "            Vinv = np.linalg.pinv(V)\n",
    "        except Exception:\n",
    "            return None\n",
    "\n",
    "        q = len(names)\n",
    "        stat = float(b.T @ Vinv @ b)\n",
    "        pval = 1.0 - stats.chi2.cdf(stat, df=q)\n",
    "        return {'stat': stat, 'df': q, 'pvalue': pval}\n",
    "\n",
    "    def _get_ci_bounds(self, results, conf_df, name, se_fallback=True):\n",
    "\n",
    "        if name not in results.params.index or conf_df is None or conf_df.empty:\n",
    "            return (np.nan, np.nan)\n",
    "\n",
    "        try:\n",
    "            if 0 in conf_df.columns and 1 in conf_df.columns:\n",
    "                lower = conf_df.loc[name, 0]\n",
    "                upper = conf_df.loc[name, 1]\n",
    "            elif 'lower' in conf_df.columns and 'upper' in conf_df.columns:\n",
    "                lower = conf_df.loc[name, 'lower']\n",
    "                upper = conf_df.loc[name, 'upper']\n",
    "            else:\n",
    "                # Fallback: first two columns by position\n",
    "                row = conf_df.loc[name]\n",
    "                lower = row.iloc[0]\n",
    "                upper = row.iloc[1]\n",
    "        except Exception:\n",
    "            lower, upper = (np.nan, np.nan)\n",
    "\n",
    "        # Optional fallback to ±1.96*SE if CI missing or inverted\n",
    "        if se_fallback and (pd.isna(lower) or pd.isna(upper) or (upper < lower)):\n",
    "            if name in results.std_errors.index:\n",
    "                se = results.std_errors[name]\n",
    "                est = results.params[name]\n",
    "                lower = est - 1.96 * se\n",
    "                upper = est + 1.96 * se\n",
    "\n",
    "        return (lower, upper)\n",
    "\n",
    "    def test_parallel_trends(self, df_matched, country, analysis_type, outcome='emp'):\n",
    "\n",
    "        print(f\"\\n--- Running Parallel Trends Test for {country} - {analysis_type} ---\")\n",
    "        df = df_matched.copy()\n",
    "\n",
    "        if outcome not in df.columns:\n",
    "            print(f\" Outcome '{outcome}' not found. Skipping parallel trends test.\")\n",
    "            return\n",
    "\n",
    "        # Ensure numeric time\n",
    "        try:\n",
    "            df['t'] = pd.to_numeric(df['t'], errors='coerce')\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "        time_periods = sorted(df['t'].dropna().unique().tolist())\n",
    "        n_periods = len(time_periods)\n",
    "        if n_periods < 2:\n",
    "            print(f\" Only {n_periods} time period(s). Skipping.\")\n",
    "            return\n",
    "\n",
    "        try:\n",
    "            max_period = max(time_periods)\n",
    "            df['first_sale_clean'] = df.groupby('id')['first_sale'].transform(lambda x: x.iloc[0])\n",
    "            df.loc[df['treatment_group'] == 0, 'first_sale_clean'] = max_period + 100\n",
    "            df['time_to_event'] = df['t'] - df['first_sale_clean']\n",
    "\n",
    "            # Unique event times (treated units only)\n",
    "            event_times_treated = sorted(df.loc[df['treatment_group'] == 1, 'time_to_event'].dropna().unique().tolist())\n",
    "            pre_periods = [t for t in event_times_treated if t < 0]\n",
    "            post_periods = [t for t in event_times_treated if t >= 0]\n",
    "\n",
    "            if len(pre_periods) == 0:\n",
    "                print(\" No pre-treatment periods for treated units. Limited ability to test parallel trends.\")\n",
    "\n",
    "            omitted_period = max(pre_periods) if len(pre_periods) > 0 else None\n",
    "            if omitted_period is not None:\n",
    "                print(f\"Event times for treated units: {event_times_treated}\")\n",
    "                print(f\"Omitted period (reference): {omitted_period}\")\n",
    "            else:\n",
    "                print(f\"Event times for treated units (no pre periods): {event_times_treated}\")\n",
    "\n",
    "            # Build event-time dummies for treated units\n",
    "            formula_parts = []\n",
    "            coef_info = []\n",
    "            for event_t in event_times_treated:\n",
    "                if omitted_period is not None and event_t == omitted_period:\n",
    "                    continue\n",
    "                dummy_name = f'treat_event_{str(event_t).replace(\"-\", \"m\")}'\n",
    "                df[dummy_name] = ((df['treatment_group'] == 1) &\n",
    "                                  (df['time_to_event'] == event_t)).astype(int)\n",
    "                formula_parts.append(dummy_name)\n",
    "                coef_info.append((dummy_name, event_t))\n",
    "\n",
    "            if not formula_parts:\n",
    "                print(\" No event-time indicators created (nothing to estimate/plot).\")\n",
    "                return\n",
    "\n",
    "            rhs = ' + '.join(formula_parts) if formula_parts else '1'\n",
    "            formula = f\"{outcome} ~ {rhs} + EntityEffects + TimeEffects\"\n",
    "\n",
    "            print(f\"\\nEvent study formula: {formula}\")\n",
    "\n",
    "            df_panel = df.set_index(['id', 't'])\n",
    "            model = PanelOLS.from_formula(formula, data=df_panel, drop_absorbed=True)\n",
    "            results = model.fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "            print(\"\\nEvent Study Results:\")\n",
    "            print(results)\n",
    "\n",
    "            # Extract coefficients and CIs for plotting\n",
    "            plot_data = []\n",
    "            conf = None\n",
    "            try:\n",
    "                conf = results.conf_int()\n",
    "            except Exception:\n",
    "                conf = None\n",
    "\n",
    "            for coef_name, event_time in coef_info:\n",
    "                if coef_name in results.params.index:\n",
    "                    lower, upper = self._get_ci_bounds(results, conf, coef_name)\n",
    "                    est = results.params[coef_name]\n",
    "                    se = results.std_errors.get(coef_name, np.nan)\n",
    "                    pval = results.pvalues.get(coef_name, np.nan)\n",
    "\n",
    "                    plot_data.append({\n",
    "                        'event_time': event_time,\n",
    "                        'coef': est,\n",
    "                        'se': se,\n",
    "                        'ci_lower': lower,\n",
    "                        'ci_upper': upper,\n",
    "                        'pval': pval\n",
    "                    })\n",
    "\n",
    "            # Add omitted period as zero line, if exists\n",
    "            if omitted_period is not None:\n",
    "                plot_data.append({\n",
    "                    'event_time': omitted_period,\n",
    "                    'coef': 0.0,\n",
    "                    'se': 0.0,\n",
    "                    'ci_lower': 0.0,\n",
    "                    'ci_upper': 0.0,\n",
    "                    'pval': 1.0\n",
    "                })\n",
    "\n",
    "            if not plot_data:\n",
    "                print(\" No coefficients extracted for plotting.\")\n",
    "            else:\n",
    "                plot_df = pd.DataFrame(plot_data).sort_values('event_time')\n",
    "\n",
    "                # Prepare yerr safely\n",
    "                yerr_low = (plot_df['coef'] - plot_df['ci_lower']).clip(lower=0).values\n",
    "                yerr_high = (plot_df['ci_upper'] - plot_df['coef']).clip(lower=0).values\n",
    "                yerr = np.vstack([yerr_low, yerr_high])\n",
    "\n",
    "                plt.figure(figsize=(10, 6))\n",
    "                plt.errorbar(plot_df['event_time'], plot_df['coef'],\n",
    "                             yerr=yerr,\n",
    "                             fmt='o', markersize=8, capsize=5, capthick=2, label='Point estimate')\n",
    "                plt.axhline(y=0, linestyle='--', alpha=0.5)\n",
    "                if omitted_period is not None:\n",
    "                    plt.axvline(x=-0.5, linestyle=':', alpha=0.5, label='Treatment begins')\n",
    "\n",
    "                for _, row in plot_df.iterrows():\n",
    "                    if omitted_period is None or row['event_time'] != omitted_period:\n",
    "                        if row['pval'] < 0.01:\n",
    "                            plt.text(row['event_time'], row['coef'] + 0.02, '***', ha='center', va='bottom', fontsize=12)\n",
    "                        elif row['pval'] < 0.05:\n",
    "                            plt.text(row['event_time'], row['coef'] + 0.02, '**', ha='center', va='bottom', fontsize=12)\n",
    "                        elif row['pval'] < 0.10:\n",
    "                            plt.text(row['event_time'], row['coef'] + 0.02, '*', ha='center', va='bottom', fontsize=12)\n",
    "\n",
    "                plt.xlabel('Time Relative to Treatment')\n",
    "                plt.ylabel(f'Effect on {outcome}')\n",
    "                plt.title(f'Event Study: {country} - {analysis_type}\\n(Parallel Trends Test)')\n",
    "                plt.grid(True, alpha=0.3)\n",
    "                plt.legend()\n",
    "\n",
    "                try:\n",
    "                    min_time = int(np.floor(plot_df['event_time'].min()))\n",
    "                    max_time = int(np.ceil(plot_df['event_time'].max()))\n",
    "                    plt.xticks(range(min_time, max_time + 1))\n",
    "                except Exception:\n",
    "                    pass\n",
    "\n",
    "                plt.tight_layout()\n",
    "                filename = f'parallel_trends_{country}_{analysis_type}.png'\n",
    "                plt.savefig(filename, dpi=300, bbox_inches='tight')\n",
    "                print(f\"\\n✅ Event study plot saved as '{filename}'\")\n",
    "                plt.show()\n",
    "\n",
    "            # Joint tests\n",
    "            pre_names = [name for name, t in coef_info if t < 0]\n",
    "            post_names = [name for name, t in coef_info if t >= 0]\n",
    "\n",
    "            if len(pre_names) > 0:\n",
    "                w_pre = self._wald_joint_test(results, pre_names)\n",
    "                if w_pre is not None:\n",
    "                    print(\"\\n📊 Joint test of pre-trends (H0: all pre-treatment effects = 0):\")\n",
    "                    print(f\"   Chi2({w_pre['df']}): {w_pre['stat']:.3f}  p-value: {w_pre['pvalue']:.3f}\")\n",
    "                    if w_pre['pvalue'] > 0.10:\n",
    "                        print(\"   ✅ PASS: No evidence of differential pre-trends\")\n",
    "                    elif w_pre['pvalue'] > 0.05:\n",
    "                        print(\"   ⚠️  MARGINAL: Weak evidence of differential pre-trends\")\n",
    "                    else:\n",
    "                        print(\"   ❌ FAIL: Significant evidence of differential pre-trends\")\n",
    "                else:\n",
    "                    print(\"\\n⚠️ Could not compute joint pre-trend test (parameters absorbed or singular).\")\n",
    "            else:\n",
    "                print(\"\\n📊 Only one or zero pre-treatment periods — joint pre-trend test not applicable.\")\n",
    "\n",
    "            if len(post_names) > 0:\n",
    "                w_post = self._wald_joint_test(results, post_names)\n",
    "                if w_post is not None:\n",
    "                    print(\"\\n📊 Joint test of post-treatment dynamics (H0: all post-treatment effects = 0):\")\n",
    "                    print(f\"   Chi2({w_post['df']}): {w_post['stat']:.3f}  p-value: {w_post['pvalue']:.3f}\")\n",
    "                else:\n",
    "                    print(\"\\n⚠️ Could not compute joint post-treatment test (parameters absorbed or singular).\")\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f\"❌ Error in parallel trends test: {e}\")\n",
    "            import traceback\n",
    "            traceback.print_exc()\n",
    "\n",
    "    def run_placebo_test(self, df_matched, country, analysis_type, outcome='emp'):\n",
    "\n",
    "        print(f\"\\n--- Running Placebo Test for {country} - {analysis_type} ---\")\n",
    "        df = df_matched.copy()\n",
    "\n",
    "        if outcome not in df.columns:\n",
    "            print(f\"⚠️ Outcome '{outcome}' not found. Skipping placebo test.\")\n",
    "            return\n",
    "\n",
    "        time_periods = sorted(df['t'].dropna().unique().tolist())\n",
    "        n_periods = len(time_periods)\n",
    "\n",
    "        placebo_results = []\n",
    "\n",
    "        if n_periods <= 1:\n",
    "            print(f\"⚠️ Only {n_periods} period. Cannot run placebo tests.\")\n",
    "            return\n",
    "\n",
    "        # Two-period case: baseline-difference test\n",
    "        if n_periods == 2:\n",
    "            print(\"Detected 2 periods. Running baseline-difference placebo test (pre-treatment equivalence).\")\n",
    "\n",
    "            min_t, max_t = time_periods[0], time_periods[1]\n",
    "            future_treated_ids = df.loc[(df['treatment_group'] == 1) & (df['first_sale'] == max_t), 'id'].unique()\n",
    "            control_ids = df.loc[df['treatment_group'] == 0, 'id'].unique()\n",
    "\n",
    "            df_base = df[df['t'] == min_t].copy()\n",
    "            df_base = df_base[df_base['id'].isin(np.r_[future_treated_ids, control_ids])]\n",
    "\n",
    "            if df_base.empty:\n",
    "                print(\"⚠️ No baseline data for placebo test.\")\n",
    "                return\n",
    "\n",
    "            df_base['future_treated'] = df_base['id'].isin(future_treated_ids).astype(int)\n",
    "\n",
    "            X = sm.add_constant(df_base['future_treated'])\n",
    "            y = df_base[outcome]\n",
    "            try:\n",
    "                ols = sm.OLS(y, X, missing='drop').fit(cov_type='HC1')  # robust SEs\n",
    "                coef = ols.params.get('future_treated', np.nan)\n",
    "                se = ols.bse.get('future_treated', np.nan)\n",
    "                pval = ols.pvalues.get('future_treated', np.nan)\n",
    "\n",
    "                placebo_results.append({\n",
    "                    'cohort': max_t,\n",
    "                    'placebo_year': min_t,\n",
    "                    'coef': coef,\n",
    "                    'se': se,\n",
    "                    'pval': pval,\n",
    "                    'n_obs': int(len(df_base)),\n",
    "                    'n_treated': int(len(future_treated_ids)),\n",
    "                    'n_control': int(len(control_ids)),\n",
    "                    'method': 'baseline-difference'\n",
    "                })\n",
    "\n",
    "                status = \"✅ PASS\" if (pd.isna(pval) or pval > 0.10) else \"⚠️ FAIL\"\n",
    "                print(f\"  Baseline difference (future-treated vs control): {coef:.4f} (SE: {se:.4f})  p={pval:.4f}  {status}\")\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"  Error running baseline-difference placebo: {e}\")\n",
    "\n",
    "        # Three-or-more periods: standard pre-only DID placebon\n",
    "        else:\n",
    "            treatment_cohorts = df.loc[df['treatment_group'] == 1].groupby('first_sale').size()\n",
    "            print(f\"Treatment cohorts: {dict(treatment_cohorts)}\")\n",
    "\n",
    "            for actual_treatment_year in treatment_cohorts.index:\n",
    "                if pd.isna(actual_treatment_year):\n",
    "                    continue\n",
    "\n",
    "                try:\n",
    "                    actual_idx = time_periods.index(actual_treatment_year)\n",
    "                    if actual_idx <= 0:\n",
    "                        continue  # No earlier period available\n",
    "                    placebo_year = time_periods[actual_idx - 1]\n",
    "                except (ValueError, IndexError):\n",
    "                    continue\n",
    "\n",
    "                print(f\"\\nTesting placebo treatment at t={placebo_year} for cohort actually treated at t={actual_treatment_year}\")\n",
    "\n",
    "                cohort_ids = df.loc[(df['first_sale'] == actual_treatment_year) &\n",
    "                                    (df['treatment_group'] == 1), 'id'].unique()\n",
    "                control_ids = df.loc[df['treatment_group'] == 0, 'id'].unique()\n",
    "\n",
    "                df_placebo = df[df['id'].isin(np.r_[cohort_ids, control_ids])].copy()\n",
    "                df_placebo = df_placebo[df_placebo['t'] < actual_treatment_year].copy()\n",
    "\n",
    "                if df_placebo['t'].nunique() < 2:\n",
    "                    print(f\"  Skipping: Not enough pre-treatment periods\")\n",
    "                    continue\n",
    "\n",
    "                df_placebo['placebo_treated'] = df_placebo['id'].isin(cohort_ids).astype(int)\n",
    "                df_placebo['post_placebo'] = (df_placebo['t'] >= placebo_year).astype(int)\n",
    "                df_placebo['did_placebo'] = df_placebo['placebo_treated'] * df_placebo['post_placebo']\n",
    "\n",
    "                try:\n",
    "                    formula = f\"{outcome} ~ did_placebo + placebo_treated + post_placebo + EntityEffects\"\n",
    "                    df_panel = df_placebo.set_index(['id', 't'])\n",
    "                    model = PanelOLS.from_formula(formula, data=df_panel, drop_absorbed=True)\n",
    "                    result = model.fit(cov_type='clustered', cluster_entity=True)\n",
    "\n",
    "                    coef = result.params.get('did_placebo', np.nan)\n",
    "                    se = result.std_errors.get('did_placebo', np.nan)\n",
    "                    pval = result.pvalues.get('did_placebo', np.nan)\n",
    "\n",
    "                    placebo_results.append({\n",
    "                        'cohort': actual_treatment_year,\n",
    "                        'placebo_year': placebo_year,\n",
    "                        'coef': coef,\n",
    "                        'se': se,\n",
    "                        'pval': pval,\n",
    "                        'n_obs': int(len(df_placebo)),\n",
    "                        'n_treated': int(len(cohort_ids)),\n",
    "                        'n_control': int(len(control_ids)),\n",
    "                        'method': 'DID-pre-only'\n",
    "                    })\n",
    "\n",
    "                    status = \"✅ PASS\" if (pd.isna(pval) or pval > 0.10) else \"⚠️ FAIL\"\n",
    "                    print(f\"  Coefficient: {coef:.4f} (SE: {se:.4f})  p={pval:.4f}  {status}\")\n",
    "\n",
    "                except Exception as e:\n",
    "                    print(f\"  Error: {e}\")\n",
    "                    continue\n",
    "\n",
    "        if placebo_results:\n",
    "            print(\"\\n--- Placebo Test Summary ---\")\n",
    "            passed = sum(1 for r in placebo_results if (pd.isna(r['pval']) or r['pval'] > 0.10))\n",
    "            total = len(placebo_results)\n",
    "            print(f\"Passed: {passed}/{total} tests\")\n",
    "\n",
    "            summary_df = pd.DataFrame(placebo_results)\n",
    "            cols = ['cohort', 'placebo_year', 'coef', 'se', 'pval', 'n_treated', 'n_control', 'method']\n",
    "            print(\"\\nDetailed Results:\")\n",
    "            print(summary_df[cols])\n",
    "\n",
    "            failed_tests = sum(1 for r in placebo_results if (not pd.isna(r['pval'])) and r['pval'] <= 0.10)\n",
    "            if failed_tests == 0:\n",
    "                print(\"✅ Overall Result: All placebo tests passed. No significant pre-treatment effects were found.\")\n",
    "            else:\n",
    "                print(f\"❌ Overall Result: {failed_tests}/{len(placebo_results)} placebo tests failed. Suggests presence of pre-trends.\")\n",
    "        else:\n",
    "            print(\"\\n⚠️ No placebo tests could be performed\")\n",
    "\n",
    "\n",
    "    def run_analysis(self):\n",
    "        \"\"\"Run the complete analysis for all countries\"\"\"\n",
    "        results = {}\n",
    "\n",
    "        for country in self.countries:\n",
    "            print(f\"\\n{'=' * 60}\")\n",
    "            print(f\"{'*' * 10} NOW RUNNING: {country} {'*' * 10}\")\n",
    "            print(f\"{'=' * 60}\")\n",
    "\n",
    "            df = self.load_data(country)\n",
    "            if df is None:\n",
    "                continue\n",
    "\n",
    "            # For non-Malawi countries, hard-filter to start from the second time period\n",
    "            # Restrict ONLY where the first wave is problematic (NOT Malawi, NOT Uganda)\n",
    "            restrict_to_second_wave = {'Ethiopia', 'Tanzania', 'Nigeria'}\n",
    "            if country in restrict_to_second_wave:\n",
    "                time_periods = sorted(df['t'].dropna().unique().tolist())\n",
    "                if len(time_periods) > 1:\n",
    "                    second_period_year = int(time_periods[1])\n",
    "                    print(f\"  Note: For {country}, analysis is restricted to start from the second wave: {second_period_year}.\")\n",
    "                    df = df[df['t'] >= second_period_year].copy()\n",
    "                else:\n",
    "                    print(f\"  Warning: {country} has fewer than two time periods. Cannot start from t=2. Skipping.\")\n",
    "                    continue\n",
    "            else:\n",
    "                print(f\"  Note: {country} is NOT restricted to start from the second wave.\")\n",
    "\n",
    "\n",
    "            results[country] = {}\n",
    "            baseline_year = sorted(df['t'].dropna().unique().tolist())[0]\n",
    "\n",
    "            # ------------------ Analysis 1: CCI ------------------\n",
    "            print(f\"\\n{'*' * 50}\")\n",
    "            print(f\"***** Analysis 1: Treatment defined by 'cci' *****\")\n",
    "            print(f\"***** (All households: Never sellers vs Late sellers) *****\")\n",
    "            print(f\"{'*' * 50}\")\n",
    "\n",
    "            if 'cci' not in df.columns:\n",
    "                print(\"Skipping CCI analysis: 'cci' column not found.\")\n",
    "            else:\n",
    "                df_cci = self.define_treatment_group(df.copy(), 'cci', country, baseline_year,\n",
    "                                                     sellers_only=False, restrict_to_cci_sellers=False)\n",
    "                if df_cci.empty or df_cci['treatment_group'].nunique() < 2:\n",
    "                    print(f\"Skipping CCI analysis for {country} due to insufficient treatment/control groups.\")\n",
    "                else:\n",
    "                    df_base = df_cci[df_cci['t'] == baseline_year].copy()\n",
    "\n",
    "                    print(f\"\\n  Baseline data check:\")\n",
    "                    print(f\"  Total households at baseline: {df_base['id'].nunique()}\")\n",
    "                    print(f\"  Total observations at baseline: {len(df_base)}\")\n",
    "                    print(f\"  Treatment group distribution at baseline:\")\n",
    "                    print(f\"    Control (0): {df_base[df_base['treatment_group'] == 0]['id'].nunique()} households\")\n",
    "                    print(f\"    Treatment (1): {df_base[df_base['treatment_group'] == 1]['id'].nunique()} households\")\n",
    "\n",
    "                    if len(df_base) == 0:\n",
    "                        print(f\"  ERROR: No baseline data found for year {baseline_year}\")\n",
    "                    else:\n",
    "                        best_method, match_results = self.select_best_matching_method(\n",
    "                            df_base, 'cci', self.covariates, min_retention=0.5, max_smd_thresh=0.1\n",
    "                        )\n",
    "\n",
    "                        if best_method and match_results:\n",
    "                            matched_ids = df_base.loc[match_results['matched'], 'id'].unique()\n",
    "                            if len(matched_ids) == 0:\n",
    "                                print(\"  No matched households. Skipping CCI DID.\")\n",
    "                            else:\n",
    "                                df_matched = df_cci[df_cci['id'].isin(matched_ids)].copy()\n",
    "\n",
    "                                print(f\"\\n--- {country}: DID Results (from 'cci' analysis) ---\")\n",
    "                                avail_covs3 = [c for c in self.covariates3 if c in df_matched.columns]\n",
    "                                missing_covs3 = [c for c in self.covariates3 if c not in df_matched.columns]\n",
    "                                if missing_covs3:\n",
    "                                    print(f\"  Missing covariates3: {missing_covs3}\")\n",
    "\n",
    "                                model_cci = self.estimate_did(df_matched, 'emp', avail_covs3, country, 'cci')\n",
    "\n",
    "                                results[country]['cci'] = {\n",
    "                                    'matching_method': best_method,\n",
    "                                    'n_matched': int(len(matched_ids)),\n",
    "                                    'model': model_cci,\n",
    "                                    'balance': match_results.get('balance_df')\n",
    "                                }\n",
    "\n",
    "                                if model_cci is not None:\n",
    "                                    self.test_parallel_trends(df_matched, country, 'cci', outcome='emp')\n",
    "                                    self.run_placebo_test(df_matched, country, 'cci', outcome='emp')\n",
    "\n",
    "            # ------------------ Analysis 2: cash_sale among CCI sellers ------------------\n",
    "            print(f\"\\n{'*' * 50}\")\n",
    "            print(f\"***** Analysis 2: Treatment defined by 'cash_sale' *****\")\n",
    "            print(f\"***** (Among CCI sellers: Never cash vs Late cash) *****\")\n",
    "            print(f\"{'*' * 50}\")\n",
    "\n",
    "            if 'cash_sale' not in df.columns:\n",
    "                print(\"Skipping cash_sale analysis: 'cash_sale' column not found.\")\n",
    "            elif 'cci' not in df.columns:\n",
    "                print(\"Skipping cash_sale analysis: 'cci' column not found (needed to filter to crop sellers).\")\n",
    "            else:\n",
    "                df_cash = self.define_treatment_group(df.copy(), 'cash_sale', country, baseline_year,\n",
    "                                                      sellers_only=False, restrict_to_cci_sellers=True)\n",
    "                if df_cash.empty or df_cash['treatment_group'].nunique() < 2:\n",
    "                    print(f\"Skipping cash_sale analysis for {country} due to insufficient treatment/control groups.\")\n",
    "                else:\n",
    "                    df_base_cash = df_cash[df_cash['t'] == baseline_year].copy()\n",
    "\n",
    "                    print(f\"\\n  Baseline data check:\")\n",
    "                    print(f\"  Total households at baseline: {df_base_cash['id'].nunique()}\")\n",
    "                    print(f\"  Total observations at baseline: {len(df_base_cash)}\")\n",
    "                    print(f\"  Treatment group distribution at baseline:\")\n",
    "                    print(f\"    Control (0): {df_base_cash[df_base_cash['treatment_group'] == 0]['id'].nunique()} households\")\n",
    "                    print(f\"    Treatment (1): {df_base_cash[df_base_cash['treatment_group'] == 1]['id'].nunique()} households\")\n",
    "\n",
    "                    if len(df_base_cash) == 0:\n",
    "                        print(f\"  ERROR: No baseline data found for year {baseline_year}\")\n",
    "                    else:\n",
    "                        best_method_cash, match_results_cash = self.select_best_matching_method(\n",
    "                            df_base_cash, 'cash_sale', self.covariates2\n",
    "                        )\n",
    "\n",
    "                        if best_method_cash and match_results_cash:\n",
    "                            matched_ids_cash = df_base_cash.loc[match_results_cash['matched'], 'id'].unique()\n",
    "                            if len(matched_ids_cash) == 0:\n",
    "                                print(\"  No matched households. Skipping cash_sale DID.\")\n",
    "                            else:\n",
    "                                df_matched_cash = df_cash[df_cash['id'].isin(matched_ids_cash)].copy()\n",
    "\n",
    "                                print(f\"\\n--- {country}: DID Results (from 'cash_sale' analysis among CCI sellers) ---\")\n",
    "                                avail_covs4 = [c for c in self.covariates4 if c in df_matched_cash.columns]\n",
    "                                missing_covs4 = [c for c in self.covariates4 if c not in df_matched_cash.columns]\n",
    "                                if missing_covs4:\n",
    "                                    print(f\"  Missing covariates4: {missing_covs4}\")\n",
    "\n",
    "                                model_cash = self.estimate_did(df_matched_cash, 'emp', avail_covs4, country, 'cash_sale')\n",
    "\n",
    "                                results[country]['cash_sale'] = {\n",
    "                                    'matching_method': best_method_cash,\n",
    "                                    'n_matched': int(len(matched_ids_cash)),\n",
    "                                    'model': model_cash,\n",
    "                                    'balance': match_results_cash.get('balance_df')\n",
    "                                }\n",
    "\n",
    "                                if model_cash is not None:\n",
    "                                    self.test_parallel_trends(df_matched_cash, country, 'cash_sale', outcome='emp')\n",
    "                                    self.run_placebo_test(df_matched_cash, country, 'cash_sale', outcome='emp')\n",
    "\n",
    "            # ------------------ Analysis 3: cc among CCI sellers - OBSOLETE, IGNORE ------------------\n",
    "            print(f\"\\n{'*' * 50}\")\n",
    "            print(f\"***** Analysis 3: Treatment defined by 'cc' *****\")\n",
    "            print(f\"***** (Among CCI sellers: Never certified vs Late certified) *****\")\n",
    "            print(f\"{'*' * 50}\")\n",
    "\n",
    "            if 'cc' not in df.columns:\n",
    "                print(\"Skipping CC analysis: 'cc' column not found.\")\n",
    "            elif 'cci' not in df.columns:\n",
    "                print(\"Skipping CC analysis: 'cci' column not found (needed to filter sellers).\")\n",
    "            else:\n",
    "                df_sellers_only = df[(df['cci'] > 0) & (df['cci'].notna())].copy()\n",
    "                if len(df_sellers_only) == 0:\n",
    "                    print(\"No CCI sellers found in the data.\")\n",
    "                else:\n",
    "                    print(f\"  Filtered to CCI sellers only: {df_sellers_only['id'].nunique()} households\")\n",
    "\n",
    "                    df_cc = self.define_treatment_group(df_sellers_only.copy(), 'cc', country, baseline_year,\n",
    "                                                        sellers_only=False, restrict_to_cci_sellers=False)\n",
    "                    if df_cc.empty or df_cc['treatment_group'].nunique() < 2:\n",
    "                        print(f\"Skipping CC analysis for {country} due to insufficient treatment/control groups.\")\n",
    "                    else:\n",
    "                        df_base_cc = df_cc[df_cc['t'] == baseline_year].copy()\n",
    "\n",
    "                        print(f\"\\n  Baseline data check:\")\n",
    "                        print(f\"  Total households at baseline: {df_base_cc['id'].nunique()}\")\n",
    "                        print(f\"  Total observations at baseline: {len(df_base_cc)}\")\n",
    "                        print(f\"  Treatment group distribution at baseline:\")\n",
    "                        print(f\"    Control (0): {df_base_cc[df_base_cc['treatment_group'] == 0]['id'].nunique()} households\")\n",
    "                        print(f\"    Treatment (1): {df_base_cc[df_base_cc['treatment_group'] == 1]['id'].nunique()} households\")\n",
    "\n",
    "                        if len(df_base_cc) == 0:\n",
    "                            print(f\"  ERROR: No baseline data found for year {baseline_year}\")\n",
    "                        else:\n",
    "                            best_method_cc, match_results_cc = self.select_best_matching_method(\n",
    "                                df_base_cc, 'cc', self.covariates2\n",
    "                            )\n",
    "\n",
    "                            if best_method_cc and match_results_cc:\n",
    "                                matched_ids_cc = df_base_cc.loc[match_results_cc['matched'], 'id'].unique()\n",
    "                                if len(matched_ids_cc) == 0:\n",
    "                                    print(\"  No matched households. Skipping CC DID.\")\n",
    "                                else:\n",
    "                                    df_matched_cc = df_cc[df_cc['id'].isin(matched_ids_cc)].copy()\n",
    "\n",
    "                                    print(f\"\\n--- {country}: DID Results (from 'cc' analysis - never vs late certified sellers among CCI sellers) ---\")\n",
    "                                    avail_covs4_cc = [c for c in self.covariates4 if c in df_matched_cc.columns]\n",
    "                                    missing_covs4_cc = [c for c in self.covariates4 if c not in df_matched_cc.columns]\n",
    "                                    if missing_covs4_cc:\n",
    "                                        print(f\"  Missing covariates4: {missing_covs4_cc}\")\n",
    "\n",
    "                                        # proceed with available ones\n",
    "                                    model_cc = self.estimate_did(df_matched_cc, 'emp', avail_covs4_cc, country, 'cc')\n",
    "\n",
    "                                    results[country]['cc'] = {\n",
    "                                        'matching_method': best_method_cc,\n",
    "                                        'n_matched': int(len(matched_ids_cc)),\n",
    "                                        'model': model_cc,\n",
    "                                        'balance': match_results_cc.get('balance_df')\n",
    "                                    }\n",
    "\n",
    "                                    if model_cc is not None:\n",
    "                                        self.test_parallel_trends(df_matched_cc, country, 'cc', outcome='emp')\n",
    "                                        self.run_placebo_test(df_matched_cc, country, 'cc', outcome='emp')\n",
    "\n",
    "        print(f\"\\n{'=' * 60}\")\n",
    "        print(f\"{'*' * 10} ALL ANALYSES COMPLETE {'*' * 10}\")\n",
    "        print(f\"{'=' * 60}\")\n",
    "\n",
    "        return results\n",
    "\n",
    "    def summarize_results(self, results):\n",
    "        summary = []\n",
    "\n",
    "        analysis_descriptions = {\n",
    "            'cci': 'Never sellers vs Late sellers',\n",
    "            'cash_sale': 'CCI sellers: Never cash vs Late cash',\n",
    "            'cc': 'CCI sellers: Never cert vs Late cert'\n",
    "        }\n",
    "\n",
    "        for country, analyses in results.items():\n",
    "            for analysis, res in analyses.items():\n",
    "                if res and res.get('model') is not None:\n",
    "                    model = res['model']\n",
    "                    balance_df = res.get('balance')\n",
    "                    mean_smd = (balance_df['smd'].mean()\n",
    "                                if balance_df is not None and not balance_df.empty else np.nan)\n",
    "                    te = model.params.get('treat_post', np.nan)\n",
    "                    se = model.std_errors.get('treat_post', np.nan)\n",
    "                    pv = model.pvalues.get('treat_post', np.nan)\n",
    "\n",
    "                    summary.append({\n",
    "                        'Country': country,\n",
    "                        'Analysis': analysis,\n",
    "                        'Description': analysis_descriptions.get(analysis, analysis),\n",
    "                        'Matching Method': res['matching_method'],\n",
    "                        'N Matched HH': res['n_matched'],\n",
    "                        'Treatment Effect': te,\n",
    "                        'SE': se,\n",
    "                        'P-value': pv,\n",
    "                        'Mean Balance (SMD)': mean_smd\n",
    "                    })\n",
    "\n",
    "        if not summary:\n",
    "            return pd.DataFrame()\n",
    "        return pd.DataFrame(summary)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "\n",
    "    data_paths = {\n",
    "        'Malawi': 'C:/Users/andre/OneDrive/Desktop/BMGF/LSMS ISA/MAP/Data Fred/Panel/MalPanel-5.dta',\n",
    "        'Ethiopia': 'C:/Users/andre/OneDrive/Desktop/BMGF/LSMS ISA/MAP/Data Fred/Panel/EthPanel-5.dta',\n",
    "        'Uganda': 'C:/Users/andre/OneDrive/Desktop/BMGF/LSMS ISA/MAP/Data Fred/Panel/UgaPanel-6.dta',\n",
    "        'Tanzania': 'C:/Users/andre/OneDrive/Desktop/BMGF/LSMS ISA/MAP/Data Fred/Panel/TanPanel-5.dta',\n",
    "        'Nigeria': 'C:/Users/andre/OneDrive/Desktop/BMGF/LSMS ISA/MAP/Data Fred/Panel/NigPanel-6.dta'\n",
    "    }\n",
    "\n",
    "    # Initialize analysis\n",
    "    analysis = PSMDIDAnalysis(data_paths)\n",
    "\n",
    "    # Run analysis\n",
    "    results = analysis.run_analysis()\n",
    "\n",
    "    # Gummary\n",
    "    if results:\n",
    "        summary = analysis.summarize_results(results)\n",
    "        print(\"\\n\\nSummary of Results:\")\n",
    "        print(summary)\n",
    "\n",
    "        if isinstance(summary, pd.DataFrame) and not summary.empty:\n",
    "            summary.to_csv('psm_did_results_summary_enhanced.csv', index=False)\n",
    "            print(\"\\nResults saved to 'psm_did_results_summary_enhanced.csv'\")\n",
    "        else:\n",
    "            print(\"\\nNo estimable models to summarize.\")\n",
    "    else:\n",
    "        print(\"Analysis did not produce any results.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ebad50e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Running Malawi ===\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andre\\AppData\\Local\\Temp\\ipykernel_16276\\1926574142.py:77: UnicodeWarning: \n",
      "One or more strings in the dta file could not be decoded using utf-8, and\n",
      "so the fallback encoding of latin-1 is being used.  This can happen when a file\n",
      "has been incorrectly encoded by Stata or some other software. You should verify\n",
      "the string values returned are correct.\n",
      "  df = pd.read_stata(path)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Malawi] baseline set to: 2011\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Malawi__cci_entry\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2013,2016,2019\n",
      "Unique t: 2011,2013,2016,2019\n",
      "[CSDID-R v2] done: csdid_outputs\\Malawi__cci_entry\n",
      "\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Malawi__cash_adoption_among_sellers\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2013,2016,2019\n",
      "Unique t: 2011,2013,2016,2019\n",
      "[CSDID-R v2] done: csdid_outputs\\Malawi__cash_adoption_among_sellers\n",
      "\n",
      "  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\n",
      "\n",
      "=== Running Ethiopia ===\n",
      "[Ethiopia] baseline set to: 2014\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Ethiopia__cci_entry\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2016\n",
      "Unique t: 2012,2014,2016\n",
      "[CSDID-R v2] done: csdid_outputs\\Ethiopia__cci_entry\n",
      "\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Ethiopia__cash_adoption_among_sellers\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2016\n",
      "Unique t: 2012,2014,2016\n",
      "Warning message:\n",
      "In compute.aggte(MP = MP, type = type, balance_e = balance_e, min_e = min_e,  :\n",
      "  Simultaneous conf. band is somehow smaller than pointwise one using normal approximation. Since this is unusual, we are reporting pointwise confidence intervals\n",
      "[CSDID-R v2] done: csdid_outputs\\Ethiopia__cash_adoption_among_sellers\n",
      "\n",
      "  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\n",
      "\n",
      "=== Running Uganda ===\n",
      "[Uganda] baseline set to: 2010\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[Uganda][cci_entry] TRY1 failed → Command '['C:\\\\Program Files\\\\R\\\\R-4.5.0\\\\bin\\\\x64\\\\Rscript.exe', 'csdid_outputs\\\\_csdid_runner_v2.R', 'csdid_outputs\\\\Uganda__cci_entry_panel.csv', 'csdid_outputs\\\\Uganda__cci_entry', 'emp', 't', 'id', 'G', '~ lognf + educh + head_age + head_sex + dependency_ratio + farmsize + asset + fadult + madult + educated + infra_index + vharvest', 'notyettreated', 'FALSE']' returned non-zero exit status 1.. Retrying with no covariates.\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=[]\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Uganda__cci_entry\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2012,2014,2016,2020\n",
      "Unique t: 2010,2012,2014,2016,2020\n",
      "[CSDID-R v2] done: csdid_outputs\\Uganda__cci_entry\n",
      "\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Uganda__cash_adoption_among_sellers\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2012,2014,2016,2020\n",
      "Unique t: 2010,2012,2014,2016,2020\n",
      "[CSDID-R v2] done: csdid_outputs\\Uganda__cash_adoption_among_sellers\n",
      "\n",
      "  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\n",
      "\n",
      "=== Running Tanzania ===\n",
      "[Tanzania] dropped periods with bad outcome: [2009]\n",
      "[Tanzania] baseline set to: 2011\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Tanzania__cci_entry\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2013,2015,2020\n",
      "Unique t: 2011,2013,2015,2020\n",
      "[CSDID-R v2] done: csdid_outputs\\Tanzania__cci_entry\n",
      "\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Tanzania__cash_adoption_among_sellers\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2013,2015,2020\n",
      "Unique t: 2011,2013,2015,2020\n",
      "[CSDID-R v2] done: csdid_outputs\\Tanzania__cash_adoption_among_sellers\n",
      "\n",
      "  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\n",
      "\n",
      "=== Running Nigeria ===\n",
      "[Nigeria] dropped periods with bad outcome: [2011]\n",
      "[Nigeria] baseline set to: 2013\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Nigeria__cci_entry\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2016,2019\n",
      "Unique t: 2013,2016,2019\n",
      "[CSDID-R v2] done: csdid_outputs\\Nigeria__cci_entry\n",
      "\n",
      "[RUN] Using R script at: C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\Emppaper\\csdid_outputs\\_csdid_runner_v2.R | ctrl=notyettreated | pretest=False | X=['lognf', 'educh', 'head_age', 'head_sex', 'dependency_ratio', 'farmsize', 'asset', 'fadult', 'madult', 'educated', 'infra_index', 'vharvest']\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "[1] TRUE\n",
      "\n",
      "[CSDID-R v2] starting for: csdid_outputs\\Nigeria__cash_adoption_among_sellers\n",
      "Warning message:\n",
      "package 'did' was built under R version 4.5.1 \n",
      "Unique G: 0,2016,2019\n",
      "Unique t: 2013,2016,2019\n",
      "[CSDID-R v2] done: csdid_outputs\\Nigeria__cash_adoption_among_sellers\n",
      "\n",
      "  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\n",
      "[CSV] Wrote csdid_summary_overall.csv\n",
      "[XLSX] Wrote consolidated workbook → csdid_outputs\\csdid_all_results.xlsx\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Callaway & Sant'Anna ATT(g,t) via R `did` — consolidated to ONE Excel workbook.\n",
    "\n",
    "Analyses per country:\n",
    "  1) CCI entry (all HH): treat = first t with cci>0, baseline cci==0; controls = not-yet-treated\n",
    "  2) CASH among sellers: restrict to ever-cci>0; treat = first t cash_sale>0, baseline cash_sale==0; controls = not-yet-treated\n",
    "  (Certification analysis DROPPED)\n",
    "\n",
    "Estimator: did::att_gt(control_group=\"notyettreated\", est_method=\"dr\"), clustered on id.\n",
    "Outputs: csdid_outputs/csdid_all_results.xlsx with:\n",
    "  - Results (overall ATT, SE, p, Ns, built-in pretrend W/Wpval)\n",
    "  - Per country × analysis (short codes): *_attgt, *_dyn, *_cal, *_smd, *_sample, *_byT, *_gt, *_nyctrl, *_cells, *_pretests\n",
    "\n",
    "Debug/robustness:\n",
    "  - Drops periods where outcome is all-NA (and optionally all-0)\n",
    "  - Can force-skip first wave for selected countries (e.g., Ethiopia)\n",
    "  - Prunes degenerate/collinear baseline covariates\n",
    "  - Drops thin (g,t) cells with too few not-yet-treated controls\n",
    "  - Multi-try fallback: (i) pruned X + notyettreated, (ii) no-X + notyettreated, (iii) no-X + nevertreated\n",
    "  - Runs conditional pretest only on balanced panels (else skipped)\n",
    "\"\"\"\n",
    "\n",
    "import os, sys, subprocess\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# ======================\n",
    "# Configuration\n",
    "# ======================\n",
    "RSCRIPT_PATH = r\"C:\\Program Files\\R\\R-4.5.0\\bin\\x64\\Rscript.exe\"  # adjust if needed\n",
    "OUTDIR = Path(\"csdid_outputs\"); OUTDIR.mkdir(parents=True, exist_ok=True)\n",
    "XLSX_PATH = OUTDIR / \"csdid_all_results.xlsx\"\n",
    "\n",
    "BASELINE_COVS = [\n",
    "    'lognf', 'educh', 'head_age', 'head_sex',\n",
    "    'dependency_ratio', 'farmsize', 'asset',\n",
    "    'fadult', 'madult', 'educated', 'infra_index', 'vharvest'\n",
    "]\n",
    "\n",
    "# Force skip earliest wave for these countries\n",
    "SKIP_FIRST_WAVE_FOR = {\"Ethiopia\"}\n",
    "\n",
    "# Optional: hard-drop specific coded periods per country (set of t values)\n",
    "FORCE_DROP_PERIODS = {\n",
    "    # \"Ethiopia\": {1},\n",
    "}\n",
    "\n",
    "# Drop whole periods where outcome is all NA or (optionally) all zero\n",
    "DROP_ALL_ZERO_PERIODS = True\n",
    "\n",
    "# Drop certification analysis entirely\n",
    "DROP_CC_ANALYSIS = True\n",
    "\n",
    "# Conditional pretest toggle (Python decides when to pass TRUE to R)\n",
    "RUN_CONDITIONAL_PRETEST = True\n",
    "\n",
    "# Minimum # of not-yet-treated controls required to keep a (g,t) cell\n",
    "MIN_NY_CONTROLS = 1  # try 3–5 if a country is very thin\n",
    "\n",
    "# Codes for nice sheet names\n",
    "CCODES = {\"Malawi\":\"MLW\",\"Ethiopia\":\"ETH\",\"Uganda\":\"UGA\",\"Tanzania\":\"TZA\",\"Nigeria\":\"NGA\"}\n",
    "AN_CODES = {\"cci_entry\":\"CCI\",\"cash_adoption_among_sellers\":\"CASH\"}\n",
    "\n",
    "\n",
    "# ======================\n",
    "# Utilities\n",
    "# ======================\n",
    "def _safe_sheet(name: str) -> str:\n",
    "    bad = '[]:*?/\\\\'\n",
    "    for ch in bad: name = name.replace(ch, '-')\n",
    "    return name[:31]\n",
    "\n",
    "def _load_country(path: str) -> pd.DataFrame:\n",
    "    if path.lower().endswith(\".dta\"):\n",
    "        df = pd.read_stata(path)\n",
    "    elif path.lower().endswith(\".csv\"):\n",
    "        df = pd.read_csv(path)\n",
    "    else:\n",
    "        raise ValueError(f\"Unsupported file type: {path}\")\n",
    "\n",
    "    for col in ['id', 't', 'emp']:\n",
    "        if col not in df.columns:\n",
    "            raise ValueError(f\"Missing required column '{col}' in {path}\")\n",
    "\n",
    "    df['id']  = pd.to_numeric(df['id'], errors='coerce')\n",
    "    df['t']   = pd.to_numeric(df['t'], errors='coerce')\n",
    "    df['emp'] = pd.to_numeric(df['emp'], errors='coerce')\n",
    "\n",
    "    for v in ['cci', 'cash_sale', 'cc']:\n",
    "        if v in df.columns:\n",
    "            df[v] = pd.to_numeric(df[v], errors='coerce').fillna(0)\n",
    "\n",
    "    if 'lognf' not in df.columns and 'Netnfcashinc' in df.columns:\n",
    "        df['lognf'] = np.log(pd.to_numeric(df['Netnfcashinc'], errors='coerce').fillna(0) + 1)\n",
    "\n",
    "    return (df.dropna(subset=['id','t'])\n",
    "              .drop_duplicates(subset=['id','t'])\n",
    "              .sort_values(['id','t'])\n",
    "              .reset_index(drop=True))\n",
    "\n",
    "def _freeze_baseline_covariates(df: pd.DataFrame, baseline_year: int, covs: list) -> pd.DataFrame:\n",
    "    covs_avail = [c for c in covs if c in df.columns]\n",
    "    if not covs_avail: return df\n",
    "    base = df.loc[df['t']==baseline_year, ['id']+covs_avail].copy()\n",
    "    for c in list(covs_avail):\n",
    "        if pd.api.types.is_numeric_dtype(base[c]):\n",
    "            if base[c].isna().all():\n",
    "                base.drop(columns=[c], inplace=True); covs_avail.remove(c)\n",
    "            else:\n",
    "                base[c] = pd.to_numeric(base[c], errors='coerce').fillna(base[c].median())\n",
    "        else:\n",
    "            base.drop(columns=[c], inplace=True); covs_avail.remove(c)\n",
    "    if not covs_avail: return df\n",
    "    base = base.set_index('id')\n",
    "    for c in covs_avail:\n",
    "        df[c] = df['id'].map(base[c])\n",
    "    return df\n",
    "\n",
    "def _restrict_to_baseline_panel(df: pd.DataFrame, baseline_year: int) -> pd.DataFrame:\n",
    "    ids = set(df.loc[df['t']==baseline_year, 'id'].unique())\n",
    "    return df[df['id'].isin(ids)].copy()\n",
    "\n",
    "def _build_cohort(df: pd.DataFrame, treat_var: str, baseline_year: int, restrict_to_sellers=False) -> pd.DataFrame:\n",
    "    df = _restrict_to_baseline_panel(df, baseline_year)\n",
    "    if restrict_to_sellers:\n",
    "        if 'cci' not in df.columns: raise ValueError(\"restrict_to_sellers=True but 'cci' not found.\")\n",
    "        ever_seller = df.groupby('id')['cci'].max() > 0\n",
    "        df = df[df['id'].isin(ever_seller[ever_seller].index)].copy()\n",
    "    if treat_var not in df.columns:\n",
    "        raise ValueError(f\"Treatment variable '{treat_var}' not found.\")\n",
    "    base_treat = df[df['t']==baseline_year].groupby('id')[treat_var].max().fillna(0)\n",
    "    untreated_ids = base_treat[base_treat==0].index\n",
    "    df = df[df['id'].isin(untreated_ids)].copy()\n",
    "    first_t = df.loc[df[treat_var] > 0].groupby('id')['t'].min()\n",
    "    df = df.merge(first_t.rename('G'), on='id', how='left')\n",
    "    df.loc[df['G'].notna() & (df['G'] <= baseline_year), 'G'] = np.nan\n",
    "    df['G'] = df['G'].fillna(0).astype(int)\n",
    "    valid_ids = df.groupby('id')['G'].first()\n",
    "    valid_ids = valid_ids[(valid_ids==0) | (valid_ids>baseline_year)].index\n",
    "    return df[df['id'].isin(valid_ids)].copy()\n",
    "\n",
    "def _prune_covariates_for_dr(df: pd.DataFrame, covs: list, tol_var=1e-12, tol_corr=0.9999) -> list:\n",
    "    covs_avail = [c for c in covs if c in df.columns]\n",
    "    if not covs_avail: return []\n",
    "    X = df[covs_avail].apply(pd.to_numeric, errors=\"coerce\")\n",
    "    good = [c for c in covs_avail if X[c].notna().sum()>1 and X[c].var(skipna=True)>tol_var]\n",
    "    if not good: return []\n",
    "    X = X[good]\n",
    "    corr = X.corr(method=\"pearson\", min_periods=50).abs()\n",
    "    to_drop, cols = set(), list(corr.columns)\n",
    "    for i in range(len(cols)):\n",
    "        if cols[i] in to_drop: continue\n",
    "        for j in range(i+1, len(cols)):\n",
    "            if cols[j] in to_drop: continue\n",
    "            if corr.iloc[i,j] >= tol_corr:\n",
    "                to_drop.add(cols[j])\n",
    "    return [c for c in cols if c not in to_drop]\n",
    "\n",
    "def _drop_periods_with_bad_outcome(df: pd.DataFrame, outcome=\"emp\", drop_all_zero=True):\n",
    "    keep_t, bad_t = [], []\n",
    "    for tt, sub in df.groupby(\"t\", sort=True):\n",
    "        x = pd.to_numeric(sub[outcome], errors=\"coerce\")\n",
    "        n = x.notna().sum()\n",
    "        if n == 0:\n",
    "            bad_t.append(tt); continue\n",
    "        if drop_all_zero:\n",
    "            nz = x.loc[x.notna()]\n",
    "            if len(nz)>0 and nz.nunique()==1 and float(nz.iloc[0]) == 0.0:\n",
    "                bad_t.append(tt); continue\n",
    "        keep_t.append(tt)\n",
    "    return df[df[\"t\"].isin(keep_t)].copy(), sorted(bad_t)\n",
    "\n",
    "def _smd_1d(x_t, x_c) -> float:\n",
    "    x_t = pd.to_numeric(pd.Series(x_t), errors='coerce').dropna()\n",
    "    x_c = pd.to_numeric(pd.Series(x_c), errors='coerce').dropna()\n",
    "    if x_t.empty or x_c.empty: return np.nan\n",
    "    mt, mc = x_t.mean(), x_c.mean()\n",
    "    vt, vc = x_t.var(ddof=1), x_c.var(ddof=1)\n",
    "    denom = np.sqrt((vt+vc)/2.0) if np.isfinite(vt) and np.isfinite(vc) else np.nan\n",
    "    if not np.isfinite(denom) or denom==0: return np.nan\n",
    "    return float((mt-mc)/denom)\n",
    "\n",
    "def _compute_smds(df: pd.DataFrame, baseline_year: int, covs: list) -> pd.DataFrame:\n",
    "    covs_avail = [c for c in covs if c in df.columns]\n",
    "    if not covs_avail: return pd.DataFrame()\n",
    "    base = df.loc[df['t']==baseline_year, ['id','G']+covs_avail].copy()\n",
    "    treat_ids = set(base.loc[base['G']>baseline_year, 'id'])\n",
    "    ctrl_ids  = set(base.loc[base['G']==0, 'id'])\n",
    "    rows=[]\n",
    "    for c in covs_avail:\n",
    "        rows.append({\"covariate\": c,\n",
    "                     \"SMD_baseline\": _smd_1d(base.loc[base['id'].isin(treat_ids), c],\n",
    "                                             base.loc[base['id'].isin(ctrl_ids),  c])})\n",
    "    smd = pd.DataFrame(rows)\n",
    "    smd['treated_ids_baseline'] = len(treat_ids)\n",
    "    smd['control_ids_baseline'] = len(ctrl_ids)\n",
    "    return smd\n",
    "\n",
    "def _sample_stats(df: pd.DataFrame, baseline_year: int):\n",
    "    out = {}\n",
    "    out[\"N_rows\"] = int(len(df))\n",
    "    out[\"N_ids\"]  = int(df['id'].nunique())\n",
    "    gid = df.groupby('id')['G'].first()\n",
    "    out[\"N_treated_ids\"] = int((gid > baseline_year).sum())\n",
    "    out[\"N_control_ids\"] = int((gid == 0).sum())\n",
    "    by_t=[]\n",
    "    for tval, sub in df.groupby('t'):\n",
    "        ids_t = sub['id'].unique()\n",
    "        gid_t = gid[gid.index.isin(ids_t)]\n",
    "        n_ctrl_notyet = int((gid_t==0).sum() + (gid_t>tval).sum())\n",
    "        by_t.append({\"t\": int(tval), \"n_rows\": int(len(sub)), \"n_ids\": int(len(ids_t)),\n",
    "                     \"n_notyet_controls\": n_ctrl_notyet,\n",
    "                     \"n_newly_treated\": int((gid_t==tval).sum())})\n",
    "    return pd.DataFrame([out]), pd.DataFrame(by_t).sort_values('t')\n",
    "\n",
    "def _design_cells_check(df: pd.DataFrame, baseline_year: int) -> pd.DataFrame:\n",
    "    gid = df.groupby('id')['G'].first()\n",
    "    tvals = sorted(df['t'].unique())\n",
    "    Gvals = sorted([g for g in df['G'].unique() if g>0])\n",
    "    rows=[]\n",
    "    for g in Gvals:\n",
    "        for t in tvals:\n",
    "            if t < g: continue\n",
    "            ids_t = set(df.loc[df['t']==t, 'id'])\n",
    "            n_treated = int(len(ids_t & set(gid[gid==g].index)))\n",
    "            n_controls = int(len(ids_t & set(gid[(gid==0)|(gid>t)].index)))\n",
    "            rows.append({\"g\": int(g), \"t\": int(t),\n",
    "                         \"n_treated_cohort\": n_treated,\n",
    "                         \"n_notyet_controls\": n_controls,\n",
    "                         \"ok\": bool(n_treated>0 and n_controls>0)})\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "def _is_balanced_panel(df: pd.DataFrame) -> bool:\n",
    "    tvals = sorted(df['t'].unique().tolist())\n",
    "    if len(tvals) <= 1: return False\n",
    "    cnt = df.groupby('id')['t'].nunique()\n",
    "    return bool((cnt == len(tvals)).all())\n",
    "\n",
    "def _drop_cells_with_insufficient_controls(df: pd.DataFrame, baseline_year: int,\n",
    "                                           min_controls: int = MIN_NY_CONTROLS,\n",
    "                                           verbose_label: str = \"\") -> pd.DataFrame:\n",
    "    gid = df.groupby('id')['G'].first()\n",
    "    tvals = sorted(df['t'].unique())\n",
    "    Gvals = sorted([g for g in df['G'].unique() if g > 0])\n",
    "    bad_pairs = []\n",
    "    for g in Gvals:\n",
    "        for t in tvals:\n",
    "            if t < g: \n",
    "                continue\n",
    "            ids_t = set(df.loc[df['t']==t, 'id'])\n",
    "            n_ctrl = len(ids_t & set(gid[(gid==0) | (gid>t)].index))\n",
    "            if n_ctrl < min_controls:\n",
    "                bad_pairs.append((g, t))\n",
    "    if bad_pairs:\n",
    "        print(f\"{verbose_label} dropping (g,t) with controls < {min_controls}: {bad_pairs[:12]}{' ...' if len(bad_pairs)>12 else ''}\")\n",
    "        drop_idx = pd.Index([])\n",
    "        for g,t in bad_pairs:\n",
    "            drop_idx = drop_idx.union(df.index[(df['G']==g) & (df['t']==t)])\n",
    "        return df.drop(index=drop_idx).copy()\n",
    "    return df\n",
    "\n",
    "\n",
    "# ======================\n",
    "# R script writer\n",
    "# ======================\n",
    "def _write_r_script(r_path: Path):\n",
    "    code = r\"\"\"\n",
    "args <- commandArgs(trailingOnly=TRUE)\n",
    "csv_path   <- args[1]\n",
    "out_prefix <- args[2]\n",
    "yname      <- args[3]\n",
    "tname      <- args[4]\n",
    "idname     <- args[5]\n",
    "gname      <- args[6]\n",
    "xformla_in <- args[7]\n",
    "ctrl_group <- args[8]                 # \"notyettreated\" or \"nevertreated\"\n",
    "do_pretest <- as.logical(args[9])     # \"TRUE\"/\"FALSE\"\n",
    "\n",
    "message(\"[CSDID-R v2] starting for: \", out_prefix)\n",
    "\n",
    "pkgs <- c(\"did\",\"data.table\",\"ggplot2\")\n",
    "for (p in pkgs) if (!requireNamespace(p, quietly=TRUE)) {\n",
    "  install.packages(p, repos=\"https://cloud.r-project.org\")\n",
    "}\n",
    "suppressPackageStartupMessages({ library(did); library(data.table); library(ggplot2) })\n",
    "\n",
    "DT <- data.table::fread(csv_path)\n",
    "DT[[tname]]  <- as.integer(DT[[tname]])\n",
    "DT[[gname]]  <- as.integer(DT[[gname]])\n",
    "DT[[idname]] <- as.numeric(DT[[idname]])\n",
    "DT <- DT[!is.na(DT[[yname]]), ]\n",
    "\n",
    "message(\"Unique G: \", paste(sort(unique(DT[[gname]])), collapse=\",\"))\n",
    "message(\"Unique t: \", paste(sort(unique(DT[[tname]])), collapse=\",\"))\n",
    "\n",
    "# ---------- DEBUG TABLES (design) ----------\n",
    "gt <- DT[, .N, by=.(G = get(gname), t = get(tname))][order(G,t)]\n",
    "data.table::fwrite(gt, paste0(out_prefix, \"_gt_counts.csv\"))\n",
    "\n",
    "ids <- unique(DT[, .(id = get(idname), G = get(gname))])\n",
    "ts  <- sort(unique(DT[[tname]]))\n",
    "ny  <- data.table(\n",
    "  t = ts,\n",
    "  n_ids_at_t = sapply(ts, function(tp) length(unique(DT[get(tname)==tp, get(idname)]))),\n",
    "  n_notyet_controls = sapply(ts, function(tp) {\n",
    "    ids_at_t <- unique(DT[get(tname)==tp, get(idname)])\n",
    "    sum( (ids$G==0 | ids$G>tp) & (ids$id %in% ids_at_t) )\n",
    "  })\n",
    ")\n",
    "data.table::fwrite(ny, paste0(out_prefix, \"_ny_controls_by_t.csv\"))\n",
    "\n",
    "cells <- data.table::CJ(g = sort(unique(DT[[gname]])), t = ts)\n",
    "cells <- cells[g>0 & t>=g]\n",
    "cells[, n_treated_cohort := sapply(1:.N, function(i){\n",
    "  gg <- cells$g[i]; tt <- cells$t[i]\n",
    "  length(unique(DT[get(gname)==gg & get(tname)==tt, get(idname)]))\n",
    "})]\n",
    "cells[, n_notyet_controls := sapply(1:.N, function(i){\n",
    "  tt <- cells$t[i]\n",
    "  ids_at_t <- unique(DT[get(tname)==tt, get(idname)])\n",
    "  sum( (ids$G==0 | ids$G>tt) & (ids$id %in% ids_at_t) )\n",
    "})]\n",
    "cells[, ok := (n_treated_cohort>0 & n_notyet_controls>0)]\n",
    "data.table::fwrite(cells[order(g,t)], paste0(out_prefix, \"_cells_check.csv\"))\n",
    "\n",
    "# ---------- Estimate ----------\n",
    "xformla <- tryCatch(as.formula(xformla_in), error=function(e) ~1)\n",
    "\n",
    "att <- att_gt(\n",
    "  yname  = yname, tname = tname, idname = idname, gname  = gname,\n",
    "  xformla = xformla, data = DT, panel = TRUE, allow_unbalanced_panel = TRUE,\n",
    "  control_group = ctrl_group, bstrap = TRUE, biters = 999,\n",
    "  clustervars = idname, est_method = \"dr\"\n",
    ")\n",
    "\n",
    "safe_write <- function(df, path) {\n",
    "  if (is.data.frame(df) && nrow(df) > 0 && ncol(df) > 0) {\n",
    "    data.table::fwrite(df, path); TRUE\n",
    "  } else { message(\"[CSDID-R v2] Skipping write (empty): \", path); FALSE }\n",
    "}\n",
    "\n",
    "# ---- attgt / overall / dynamic / calendar (with p-values) ----\n",
    "attgt_df <- data.frame(group = att$group, time = att$t, att = att$att, se = att$se)\n",
    "if (nrow(attgt_df) > 0) {\n",
    "  ok <- is.finite(attgt_df$se) & attgt_df$se > 0\n",
    "  tstat <- rep(NA_real_, nrow(attgt_df)); tstat[ok] <- attgt_df$att[ok] / attgt_df$se[ok]\n",
    "  attgt_df$tstat <- tstat; attgt_df$pval  <- 2*pnorm(-abs(tstat))\n",
    "}\n",
    "safe_write(attgt_df, paste0(out_prefix, \"_attgt.csv\"))\n",
    "\n",
    "ov <- aggte(att, type=\"simple\")\n",
    "ov_df <- data.frame(term=\"overall\", att=ov$overall.att, se=ov$overall.se)\n",
    "tstat <- if (is.finite(ov_df$se) && ov_df$se > 0) ov_df$att/ov_df$se else NA_real_\n",
    "ov_df$tstat <- tstat; ov_df$pval <- 2*pnorm(-abs(tstat))\n",
    "safe_write(ov_df, paste0(out_prefix, \"_overall.csv\"))\n",
    "\n",
    "dyn <- aggte(att, type=\"dynamic\")\n",
    "dyn_df <- data.frame(event_time = dyn$egt, att = dyn$att.egt, se = dyn$se.egt)\n",
    "if (nrow(dyn_df) > 0) {\n",
    "  ok <- is.finite(dyn_df$se) & dyn_df$se > 0\n",
    "  tstat <- rep(NA_real_, nrow(dyn_df)); tstat[ok] <- dyn_df$att[ok] / dyn_df$se[ok]\n",
    "  dyn_df$tstat <- tstat; dyn_df$pval <- 2*pnorm(-abs(tstat))\n",
    "}\n",
    "safe_write(dyn_df, paste0(out_prefix, \"_dynamic.csv\"))\n",
    "\n",
    "cal <- aggte(att, type=\"calendar\")\n",
    "cal_df <- data.frame(period = cal$egt, att = cal$att.egt, se = cal$se.egt)\n",
    "if (nrow(cal_df) > 0) {\n",
    "  ok <- is.finite(cal_df$se) & cal_df$se > 0\n",
    "  tstat <- rep(NA_real_, nrow(cal_df)); tstat[ok] <- cal_df$att[ok] / cal_df$se[ok]\n",
    "  cal_df$tstat <- tstat; cal_df$pval <- 2*pnorm(-abs(tstat))\n",
    "}\n",
    "safe_write(cal_df, paste0(out_prefix, \"_calendar.csv\"))\n",
    "\n",
    "# ---- built-in pretest (W/Wpval) ----\n",
    "pre_overall <- data.frame(W = att$W, Wpval = att$Wpval, alp = att$alp)\n",
    "safe_write(pre_overall, paste0(out_prefix, \"_pretest_overall.csv\"))\n",
    "\n",
    "# ---- conditional pre-test: run only if do_pretest==TRUE and panel appears balanced\n",
    "if (do_pretest) {\n",
    "  tvals <- sort(unique(DT[[tname]])); k <- length(tvals)\n",
    "  bal <- FALSE\n",
    "  if (k > 1) {\n",
    "    cnt <- DT[, uniqueN(get(tname)), by=.(id = get(idname))]\n",
    "    bal <- all(cnt$V1 == k)\n",
    "  }\n",
    "  if (!bal) {\n",
    "    message(\"conditional_did_pretest: skipped (unbalanced panel).\")\n",
    "  } else {\n",
    "    cpre <- tryCatch(\n",
    "      conditional_did_pretest(\n",
    "        yname=yname, tname=tname, idname=idname, gname=gname,\n",
    "        xformla=xformla, data=DT, panel=TRUE, allow_unbalanced_panel=FALSE,\n",
    "        control_group=ctrl_group, bstrap=TRUE, cband=TRUE, biters=999,\n",
    "        clustervars=idname, est_method=\"dr\", print_details=FALSE, cores=1\n",
    "      ),\n",
    "      error=function(e){ message(\"conditional_did_pretest error: \", e$message); NULL }\n",
    "    )\n",
    "    if (!is.null(cpre)) {\n",
    "      cp <- list(\n",
    "        CvM    = tryCatch(cpre$CvM,    error=function(e) NA_real_),\n",
    "        CvMcrit= tryCatch(cpre$CvMcval,error=function(e) NA_real_),\n",
    "        CvMpval= tryCatch(cpre$CvMpval,error=function(e) NA_real_),\n",
    "        KS     = tryCatch(cpre$KS,     error=function(e) NA_real_),\n",
    "        KScrt  = tryCatch(cpre$KScval, error=function(e) NA_real_),\n",
    "        KSpval = tryCatch(cpre$KSpval, error=function(e) NA_real_)\n",
    "      )\n",
    "      cpre_df <- data.frame(cp, check.names=FALSE)\n",
    "      safe_write(cpre_df, paste0(out_prefix, \"_pretest_conditional.csv\"))\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\n",
    "# ---- plot ----\n",
    "p <- ggdid(dyn) + ggtitle(paste0(\"Event-study: \", out_prefix))\n",
    "ggsave(filename=paste0(out_prefix, \"_dynamic.png\"), plot=p, width=7, height=5, dpi=300)\n",
    "\n",
    "message(\"[CSDID-R v2] done: \", out_prefix)\n",
    "\"\"\"\n",
    "    r_path.write_text(code, encoding=\"utf-8\")\n",
    "\n",
    "\n",
    "# ======================\n",
    "# R runner (with fallbacks)\n",
    "# ======================\n",
    "def _run_did_once(df: pd.DataFrame, country: str, analysis: str, baseline_year: int,\n",
    "                  covs: list, control_group: str, do_pretest: bool) -> dict:\n",
    "    tag = f\"{country}__{analysis}\"\n",
    "    tmp_csv   = OUTDIR / f\"{tag}_panel.csv\"\n",
    "    out_prefix= OUTDIR / f\"{tag}\"\n",
    "\n",
    "    covs_avail = _prune_covariates_for_dr(df, covs)\n",
    "    xformla = \"~ 1\" if not covs_avail else \"~ \" + \" + \".join(covs_avail)\n",
    "    df[['id','t','emp','G'] + covs_avail].to_csv(tmp_csv, index=False)\n",
    "\n",
    "    r_script = OUTDIR / \"_csdid_runner_v2.R\"\n",
    "    _write_r_script(r_script)\n",
    "    print(f\"[RUN] Using R script at: {r_script.resolve()} | ctrl={control_group} | pretest={do_pretest} | X={covs_avail}\")\n",
    "\n",
    "    cmd = [RSCRIPT_PATH, str(r_script), str(tmp_csv), str(out_prefix), \"emp\",\"t\",\"id\",\"G\",\n",
    "           xformla, control_group, \"TRUE\" if do_pretest else \"FALSE\"]\n",
    "    completed = subprocess.run(cmd, check=True, capture_output=True, text=True)\n",
    "    if completed.stdout: print(completed.stdout)\n",
    "    if completed.stderr: print(completed.stderr)\n",
    "\n",
    "    def _read(name):\n",
    "        f = OUTDIR / f\"{tag}_{name}.csv\"\n",
    "        return pd.read_csv(f) if f.exists() and f.stat().st_size>0 else None\n",
    "\n",
    "    out = {\n",
    "        \"attgt\":    _read(\"attgt\"),\n",
    "        \"overall\":  _read(\"overall\"),\n",
    "        \"dynamic\":  _read(\"dynamic\"),\n",
    "        \"calendar\": _read(\"calendar\"),\n",
    "        \"pre_over\": _read(\"pretest_overall\"),\n",
    "        \"pre_cond\": _read(\"pretest_conditional\"),\n",
    "        \"gt_counts\":_read(\"gt_counts\"),\n",
    "        \"ny_ctrl\":  _read(\"ny_controls_by_t\"),\n",
    "        \"cells\":    _read(\"cells_check\"),\n",
    "        \"baseline_year\": baseline_year,\n",
    "        \"covariates_used\": covs_avail\n",
    "    }\n",
    "    return out\n",
    "\n",
    "def _run_did_with_fallbacks(df: pd.DataFrame, country: str, analysis: str,\n",
    "                            baseline_year: int, covs: list, is_balanced: bool) -> dict:\n",
    "    # 1) not-yet-treated + pruned X; pretest only if balanced & allowed\n",
    "    try:\n",
    "        return _run_did_once(df, country, analysis, baseline_year, covs, \"notyettreated\",\n",
    "                             (RUN_CONDITIONAL_PRETEST and is_balanced))\n",
    "    except subprocess.CalledProcessError as e:\n",
    "        print(f\"[{country}][{analysis}] TRY1 failed → {e}. Retrying with no covariates.\")\n",
    "    except Exception as e:\n",
    "        print(f\"[{country}][{analysis}] TRY1 unexpected error → {e}. Retrying with no covariates.\")\n",
    "\n",
    "    # 2) no covariates\n",
    "    try:\n",
    "        return _run_did_once(df[['id','t','emp','G']].copy(), country, analysis, baseline_year, [],\n",
    "                             \"notyettreated\", (RUN_CONDITIONAL_PRETEST and is_balanced))\n",
    "    except subprocess.CalledProcessError as e:\n",
    "        print(f\"[{country}][{analysis}] TRY2 failed → {e}. Retrying with control_group='nevertreated' (no covariates).\")\n",
    "    except Exception as e:\n",
    "        print(f\"[{country}][{analysis}] TRY2 unexpected error → {e}. Retrying with control_group='nevertreated' (no covariates).\")\n",
    "\n",
    "    # 3) fallback: nevertreated, no covariates\n",
    "    return _run_did_once(df[['id','t','emp','G']].copy(), country, analysis, baseline_year, [],\n",
    "                         \"nevertreated\", (RUN_CONDITIONAL_PRETEST and is_balanced))\n",
    "\n",
    "\n",
    "# ======================\n",
    "# Orchestrator\n",
    "# ======================\n",
    "def run_country(country: str, path: str) -> dict:\n",
    "    print(f\"\\n=== Running {country} ===\")\n",
    "    df = _load_country(path)\n",
    "\n",
    "    if country in FORCE_DROP_PERIODS and FORCE_DROP_PERIODS[country]:\n",
    "        df = df[~df[\"t\"].isin(FORCE_DROP_PERIODS[country])].copy()\n",
    "        print(f\"[{country}] force-dropped periods: {sorted(FORCE_DROP_PERIODS[country])}\")\n",
    "\n",
    "    df, dropped = _drop_periods_with_bad_outcome(df, outcome=\"emp\", drop_all_zero=DROP_ALL_ZERO_PERIODS)\n",
    "    if dropped: print(f\"[{country}] dropped periods with bad outcome: {dropped}\")\n",
    "\n",
    "    years = sorted(df['t'].dropna().unique().tolist())\n",
    "    if not years: raise ValueError(\"No time periods left after cleaning.\")\n",
    "    baseline = int(years[1] if (country in SKIP_FIRST_WAVE_FOR and len(years)>1) else years[0])\n",
    "    print(f\"[{country}] baseline set to: {baseline}\")\n",
    "\n",
    "    df = _freeze_baseline_covariates(df, baseline, BASELINE_COVS)\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    # --- CCI\n",
    "    if 'cci' in df.columns:\n",
    "        df1 = _build_cohort(df.copy(), 'cci', baseline, restrict_to_sellers=False)\n",
    "        head_stats, by_t = _sample_stats(df1, baseline)\n",
    "        smd = _compute_smds(df1, baseline, BASELINE_COVS)\n",
    "        cells_py = _design_cells_check(df1, baseline)\n",
    "\n",
    "        if df1['G'].nunique()>=2 and df1['emp'].notna().any():\n",
    "            dfX = _drop_cells_with_insufficient_controls(df1, baseline, MIN_NY_CONTROLS, verbose_label=f\"[{country}][CCI]\")\n",
    "            is_bal = _is_balanced_panel(dfX)\n",
    "            r_out = _run_did_with_fallbacks(dfX, country, \"cci_entry\", baseline, BASELINE_COVS, is_bal)\n",
    "            r_out[\"sample_head\"]=head_stats; r_out[\"sample_byT\"]=by_t; r_out[\"smd\"]=smd; r_out[\"cells_py\"]=cells_py\n",
    "            results['cci_entry'] = r_out\n",
    "        else:\n",
    "            print(\"  [CCI] Insufficient variation for ATT(g,t); skipping.\")\n",
    "    else:\n",
    "        print(\"  [CCI] 'cci' not found; skipping.\")\n",
    "\n",
    "    # --- CASH\n",
    "    if 'cash_sale' in df.columns and 'cci' in df.columns:\n",
    "        df2 = _build_cohort(df.copy(), 'cash_sale', baseline, restrict_to_sellers=True)\n",
    "        head_stats, by_t = _sample_stats(df2, baseline)\n",
    "        smd = _compute_smds(df2, baseline, BASELINE_COVS)\n",
    "        cells_py = _design_cells_check(df2, baseline)\n",
    "\n",
    "        if df2['G'].nunique()>=2 and df2['emp'].notna().any():\n",
    "            dfX = _drop_cells_with_insufficient_controls(df2, baseline, MIN_NY_CONTROLS, verbose_label=f\"[{country}][CASH]\")\n",
    "            is_bal = _is_balanced_panel(dfX)\n",
    "            r_out = _run_did_with_fallbacks(dfX, country, \"cash_adoption_among_sellers\", baseline, BASELINE_COVS, is_bal)\n",
    "            r_out[\"sample_head\"]=head_stats; r_out[\"sample_byT\"]=by_t; r_out[\"smd\"]=smd; r_out[\"cells_py\"]=cells_py\n",
    "            results['cash_adoption_among_sellers'] = r_out\n",
    "        else:\n",
    "            print(\"  [CASH] Insufficient variation for ATT(g,t); skipping.\")\n",
    "    else:\n",
    "        print(\"  [CASH] vars missing; skipping.\")\n",
    "\n",
    "    # --- CERT dropped\n",
    "    if DROP_CC_ANALYSIS:\n",
    "        print(\"  [CERT] Dropped by config (DROP_CC_ANALYSIS=True).\")\n",
    "\n",
    "    return {\"baseline\": baseline, \"results\": results}\n",
    "\n",
    "def _write_excel(all_results: dict):\n",
    "    writer = pd.ExcelWriter(XLSX_PATH, engine=\"openpyxl\", mode=\"w\")\n",
    "    written_sheets = []\n",
    "\n",
    "    # Results summary rows\n",
    "    summary_rows = []\n",
    "    for country, payload in all_results.items():\n",
    "        ccode = CCODES.get(country, country[:3].upper())\n",
    "        baseline = payload[\"baseline\"]\n",
    "        for analysis, out in payload[\"results\"].items():\n",
    "            ancode = AN_CODES.get(analysis, analysis[:5].upper())\n",
    "\n",
    "            # overall\n",
    "            ov = out.get(\"overall\")\n",
    "            att=se=p_over=np.nan\n",
    "            if isinstance(ov, pd.DataFrame) and not ov.empty:\n",
    "                att = ov.loc[0,\"att\"]; se = ov.loc[0,\"se\"]\n",
    "                p_over = ov.loc[0,\"pval\"] if \"pval\" in ov.columns else np.nan\n",
    "\n",
    "            # Ns\n",
    "            head = out.get(\"sample_head\")\n",
    "            N_ids=N_rows=N_treated=N_ctrl=np.nan\n",
    "            if isinstance(head, pd.DataFrame) and not head.empty:\n",
    "                N_rows = int(head.loc[0,\"N_rows\"]); N_ids = int(head.loc[0,\"N_ids\"])\n",
    "                N_treated = int(head.loc[0,\"N_treated_ids\"]); N_ctrl = int(head.loc[0,\"N_control_ids\"])\n",
    "\n",
    "            # pretests\n",
    "            pre_over = out.get(\"pre_over\")\n",
    "            W = Wp = alp = np.nan\n",
    "            if isinstance(pre_over, pd.DataFrame) and not pre_over.empty:\n",
    "                W  = pre_over.loc[0,\"W\"] if \"W\" in pre_over.columns else np.nan\n",
    "                Wp = pre_over.loc[0,\"Wpval\"] if \"Wpval\" in pre_over.columns else np.nan\n",
    "                alp= pre_over.loc[0,\"alp\"] if \"alp\" in pre_over.columns else np.nan\n",
    "\n",
    "            # covs used\n",
    "            covs_used = \", \".join(out.get(\"covariates_used\", [])) if isinstance(out.get(\"covariates_used\"), list) else \"\"\n",
    "\n",
    "            summary_rows.append({\n",
    "                \"Country\": country, \"CCode\": ccode, \"Analysis\": analysis, \"ACode\": ancode,\n",
    "                \"BaselineYear\": baseline,\n",
    "                \"ATT_overall\": att, \"SE_overall\": se, \"P_overall\": p_over,\n",
    "                \"W_pretest\": W, \"W_pval\": Wp, \"alpha\": alp,\n",
    "                \"N_ids\": N_ids, \"N_rows\": N_rows, \"N_treated_ids\": N_treated, \"N_control_ids\": N_ctrl,\n",
    "                \"CovariatesUsed\": covs_used\n",
    "            })\n",
    "\n",
    "            # per-analysis sheets (write only if exist)\n",
    "            sections = [\n",
    "                (\"attgt\", out.get(\"attgt\")),\n",
    "                (\"dyn\",   out.get(\"dynamic\")),\n",
    "                (\"cal\",   out.get(\"calendar\")),\n",
    "                (\"smd\",   out.get(\"smd\")),\n",
    "                (\"sample\",out.get(\"sample_head\")),\n",
    "                (\"byT\",   out.get(\"sample_byT\")),\n",
    "                (\"gt\",    out.get(\"gt_counts\") if out.get(\"gt_counts\") is not None else out.get(\"cells_py\")),\n",
    "                (\"nyctrl\",out.get(\"ny_ctrl\")),\n",
    "                (\"cells\", out.get(\"cells\") if out.get(\"cells\") is not None else out.get(\"cells_py\")),\n",
    "            ]\n",
    "            # combine pretests in one small sheet\n",
    "            pre_df = None\n",
    "            if isinstance(out.get(\"pre_over\"), pd.DataFrame) and not out[\"pre_over\"].empty:\n",
    "                pre_df = out[\"pre_over\"].copy()\n",
    "            if isinstance(out.get(\"pre_cond\"), pd.DataFrame) and not out[\"pre_cond\"].empty:\n",
    "                tmp = out[\"pre_cond\"].copy()\n",
    "                tmp.columns = [f\"cond_{c}\" for c in tmp.columns]\n",
    "                pre_df = tmp if pre_df is None else pd.concat([pre_df, tmp], axis=1)\n",
    "            if pre_df is not None:\n",
    "                sections.append((\"pretests\", pre_df))\n",
    "\n",
    "            for label, df in sections:\n",
    "                if isinstance(df, pd.DataFrame) and not df.empty:\n",
    "                    sheet = _safe_sheet(f\"{ccode}_{ancode}_{label}\")\n",
    "                    df.to_excel(writer, index=False, sheet_name=sheet)\n",
    "                    written_sheets.append(sheet)\n",
    "\n",
    "    # Results sheet\n",
    "    summary = pd.DataFrame(summary_rows).sort_values([\"Country\",\"Analysis\"]).reset_index(drop=True)\n",
    "    summary.to_excel(writer, index=False, sheet_name=\"Results\")\n",
    "    written_sheets.append(\"Results\")\n",
    "\n",
    "    # Contents page\n",
    "    contents = pd.DataFrame({\"Sheet\": written_sheets})\n",
    "    contents.to_excel(writer, index=False, sheet_name=_safe_sheet(\"CONTENTS\"))\n",
    "\n",
    "    writer.close()\n",
    "    print(f\"[XLSX] Wrote consolidated workbook → {XLSX_PATH}\")\n",
    "\n",
    "def summarize_csv_like(all_results: dict) -> pd.DataFrame:\n",
    "    rows=[]\n",
    "    for country, payload in all_results.items():\n",
    "        baseline = payload[\"baseline\"]\n",
    "        for analysis, out in payload[\"results\"].items():\n",
    "            ov = out.get(\"overall\")\n",
    "            if isinstance(ov, pd.DataFrame) and not ov.empty:\n",
    "                rows.append({\n",
    "                    \"Country\": country,\n",
    "                    \"Analysis\": analysis,\n",
    "                    \"ATT_overall\": ov.loc[0, \"att\"],\n",
    "                    \"SE_overall\": ov.loc[0, \"se\"],\n",
    "                    \"P_overall\": ov.loc[0, \"pval\"] if \"pval\" in ov.columns else np.nan,\n",
    "                    \"BaselineYear\": baseline,\n",
    "                    \"CovariatesUsed\": \", \".join(out.get(\"covariates_used\", [])) if isinstance(out.get(\"covariates_used\"), list) else \"\"\n",
    "                })\n",
    "    return pd.DataFrame(rows)\n",
    "\n",
    "\n",
    "# ======================\n",
    "# Main\n",
    "# ======================\n",
    "if __name__ == \"__main__\":\n",
    "    # Fill in your file paths\n",
    "    data_paths = {\n",
    "        'Malawi':   r'C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\MalPanel-5.dta',\n",
    "        'Ethiopia': r'C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\EthPanel-5.dta',\n",
    "        'Uganda':   r'C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\UgaPanel-6.dta',\n",
    "        'Tanzania': r'C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\TanPanel-5.dta',\n",
    "        'Nigeria':  r'C:\\Users\\andre\\OneDrive\\Desktop\\BMGF\\LSMS ISA\\MAP\\Data Fred\\Panel\\NigPanel-6.dta'\n",
    "    }\n",
    "\n",
    "    all_results = {}\n",
    "    for ctry, path in data_paths.items():\n",
    "        try:\n",
    "            payload = run_country(ctry, path)\n",
    "            all_results[ctry] = payload\n",
    "        except Exception as e:\n",
    "            print(f\"[ERROR] {ctry}: {e}\")\n",
    "\n",
    "    if all_results:\n",
    "        summary = summarize_csv_like(all_results)\n",
    "        if not summary.empty:\n",
    "            summary.to_csv(OUTDIR / \"csdid_summary_overall.csv\", index=False)\n",
    "            print(f\"[CSV] Wrote csdid_summary_overall.csv\")\n",
    "        _write_excel(all_results)\n",
    "    else:\n",
    "        print(\"No results produced.\")\n"
   ]
  }
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